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DistrictNet: Decision-aware learning for geographical districting | https://openreview.net/forum?id=njwYBFau8E | https://openreview.net/forum?id=njwYBFau8E | Cheikh Ahmed,Alexandre Forel,Axel Parmentier,Thibaut Vidal | NIPS 2024,Poster | Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts. In logistics, it is a major strategic decision determining operating costs for several years. Solving districting problems using traditional methods is intractable even for small geographical areas and existing heuristics often provide sub-optimal results. We present a structured learning approach to find high-quality solutions to real-world districting problems in a few minutes. It is based on integrating a combinatorial optimization layer, the capacitated minimum spanning tree problem, into a graph neural network architecture. To train this pipeline in a decision-aware fashion, we show how to construct target solutions embedded in a suitable space and learn from target solutions. Experiments show that our approach outperforms existing methods as it can significantly reduce costs on real-world cities. | https://openreview.net/pdf/f64d0fcbdecbca899d8871dd921130e3f6b558b9.pdf |
Optimal Algorithms for Learning Partitions with Faulty Oracles | https://openreview.net/forum?id=ygDl8q02gA | https://openreview.net/forum?id=ygDl8q02gA | Adela Frances DePavia,Olga Medrano Martín del Campo,Erasmo Tani | NIPS 2024,Poster | We consider a clustering problem where a learner seeks to partition a finite set by querying a faulty oracle. This models applications where learners crowdsource information from non-expert human workers or conduct noisy experiments to determine group structure. The learner aims to exactly recover a partition by submitting queries of the form ``are $u$ and $v$ in the same group?'' for any pair of elements $u$ and $v$ in the set. Moreover, because the learner only has access to faulty sources of information, they require an error-tolerant algorithm for this task: i.e. they must fully recover the correct partition, even if up to $\ell$ answers are incorrect, for some error-tolerance parameter $\ell$. We study the question: for any given error-tolerance $\ell$, what is the minimum number of queries needed to learn a finite set partition of $n$ elements into $k$ groups? We design algorithms for this task and prove that they achieve optimal query complexity. To analyze our algorithms, we first highlight a connection between this task and correlation clustering. We then use this connection to build a Rényi-Ulam style analytical framework for this problem, which yields matching lower bounds. Our analysis also reveals an inherent asymmetry between the query complexity necessary to be robust against false negative errors as opposed to false positive errors. | https://openreview.net/pdf/2abac03a655a803c28aaeea7d0cefd63e537be44.pdf |
DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models | https://openreview.net/forum?id=81IFFsfQUj | https://openreview.net/forum?id=81IFFsfQUj | Hengkang Wang,Xu Zhang,Taihui Li,Yuxiang Wan,Tiancong Chen,Ju Sun | NIPS 2024,Poster | Pretrained diffusion models (DMs) have recently been popularly used in solving inverse problems (IPs). The existing methods mostly interleave iterative steps in the reverse diffusion process and iterative steps to bring the iterates closer to satisfying the measurement constraint. However, such interleaving methods struggle to produce final results that look like natural objects of interest (i.e., manifold feasibility) and fit the measurement (i.e., measurement feasibility), especially for nonlinear IPs. Moreover, their capabilities to deal with noisy IPs with unknown types and levels of measurement noise are unknown. In this paper, we advocate viewing the reverse process in DMs as a function and propose a novel plug-in method for solving IPs using pretrained DMs, dubbed DMPlug. DMPlug addresses the issues of manifold feasibility and measurement feasibility in a principled manner, and also shows great potential for being robust to unknown types and levels of noise. Through extensive experiments across various IP tasks, including two linear and three nonlinear IPs, we demonstrate that DMPlug consistently outperforms state-of-the-art methods, often by large margins especially for nonlinear IPs. | https://openreview.net/pdf/417ea67c80aa8472615bf862fe2af28a3fba492f.pdf |
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation | https://openreview.net/forum?id=18FGRNd0wZ | https://openreview.net/forum?id=18FGRNd0wZ | Keqiang Yan,Xiner Li,Hongyi Ling,Kenna Ashen,Carl Edwards,Raymundo Arroyave,Marinka Zitnik,Heng Ji,Xiaofeng Qian,Xiaoning Qian,Shuiwang Ji | NIPS 2024,Poster | We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods. | https://openreview.net/pdf/5ba6a7cb433f251e0cb54187f36a69682d004aa0.pdf |
Achieving Domain-Independent Certified Robustness via Knowledge Continuity | https://openreview.net/forum?id=v07KRLYxDX | https://openreview.net/forum?id=v07KRLYxDX | Alan Sun,Chiyu Ma,Kenneth Ge,Soroush Vosoughi | NIPS 2024,Poster | We present *knowledge continuity*, a novel definition inspired by Lipschitz continuity which aims to certify the robustness of neural networks across input domains (such as continuous and discrete domains in vision and language, respectively). Most existing approaches that seek to certify robustness, especially Lipschitz continuity, lie within the continuous domain with norm and distribution-dependent guarantees. In contrast, our proposed definition yields certification guarantees that depend only on the loss function and the intermediate learned metric spaces of the neural network. These bounds are independent of domain modality, norms, and distribution. We further demonstrate that the expressiveness of a model class is not at odds with its knowledge continuity. This implies that achieving robustness by maximizing knowledge continuity should not theoretically hinder inferential performance. Finally, to complement our theoretical results, we present several applications of knowledge continuity such as regularization, a certification algorithm, and show that knowledge continuity can be used to localize vulnerable components of a neural network. | https://openreview.net/pdf/5637147709875e41d94cb35d3fb0ef47bf7ebba5.pdf |
BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling | https://openreview.net/forum?id=haSKMlrbX5 | https://openreview.net/forum?id=haSKMlrbX5 | Lin Gui,Cristina Garbacea,Victor Veitch | NIPS 2024,Poster | This paper concerns the problem of aligning samples from large language models to human preferences using *best-of-$n$* sampling, where we draw $n$ samples, rank them, and return the best one. We consider two fundamental problems. First: what is the relationship between best-of-$n$ and other (RLHF-type) approaches to aligning LLMs? In particular, when should one be preferred to the other? We show that the best-of-$n$ sampling distribution is essentially equivalent to the policy learned by RLHF if we apply a particular monotone transformation to the reward function. Moreover, we show that this transformation yields the best possible trade-off between win-rate against the base model vs KL distance from the base model. Then, best-of-$n$ is a Pareto-optimal win-rate vs KL solution.
The second problem we consider is how to fine-tune a model to mimic the best-of-$n$ sampling distribution, to avoid drawing $n$ samples for each inference. We derive *BonBon Alignment* as a method for achieving this. Experiments show that BonBon alignment yields a model that achieves high win rates while minimally affecting off-target aspects of the generations. | https://openreview.net/pdf/77322ec05b42e4b97cc959fb943934f0a32388b0.pdf |
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference | https://openreview.net/forum?id=tDvFa5OJyS | https://openreview.net/forum?id=tDvFa5OJyS | Jonathan Wenger,Kaiwen Wu,Philipp Hennig,Jacob R. Gardner,Geoff Pleiss,John Patrick Cunningham | NIPS 2024,Poster | Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory.
While many approximations exist, all incur inevitable approximation error.
Recent work accounts for this error in the form of computational uncertainty, which enables---at the cost of quadratic complexity---an explicit tradeoff between computation and precision.
Here we extend this development to model selection, which requires significant enhancements to the existing approach, including linear-time scaling in the size of the dataset.
We propose a novel training loss for hyperparameter optimization and demonstrate empirically that the resulting method can outperform SGPR, CGGP and SVGP, state-of-the-art methods for GP model selection, on medium to large-scale datasets.
Our experiments show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU.
As a result of this work, Gaussian processes can be trained on large-scale datasets without significantly compromising their ability to quantify uncertainty---a fundamental prerequisite for optimal decision-making. | https://openreview.net/pdf/9fd16df1a39147c5ed0812c4231d133453a8f9ab.pdf |
When is an Embedding Model More Promising than Another? | https://openreview.net/forum?id=VqFz7iTGcl | https://openreview.net/forum?id=VqFz7iTGcl | Maxime DARRIN,Philippe Formont,Ismail Ben Ayed,Jackie CK Cheung,Pablo Piantanida | NIPS 2024,Poster | Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to perform various downstream tasks. The evaluation of embedding models typically depends on domain-specific empirical approaches utilizing downstream tasks, primarily because of the lack of a standardized framework for comparison. However, acquiring adequately large and representative datasets for conducting these assessments is not always viable and can prove to be prohibitively expensive and time-consuming. In this paper, we present a unified approach to evaluate embedders. First, we establish theoretical foundations for comparing embedding models, drawing upon the concepts of sufficiency and informativeness. We then leverage these concepts to devise a tractable comparison criterion (information sufficiency), leading to a task-agnostic and self-supervised ranking procedure. We demonstrate experimentally that our approach aligns closely with the capability of embedding models to facilitate various downstream tasks in both natural language processing and molecular biology. This effectively offers practitioners a valuable tool for prioritizing model trials. | https://openreview.net/pdf/cf6fa76e8219524c5bf4afc2931bd636ada5b79e.pdf |
On the Expressive Power of Tree-Structured Probabilistic Circuits | https://openreview.net/forum?id=suYAAOI5bd | https://openreview.net/forum?id=suYAAOI5bd | Lang Yin,Han Zhao | NIPS 2024,Poster | Probabilistic circuits (PCs) have emerged as a powerful framework compactly representing probability distributions for efficient and exact probabilistic inference. It has been shown that PCs with general directed acyclic graph (DAG) structure can be understood as a mixture of exponentially (in its height) many components, each of which is a product distributions over univariate marginals. However, existing structure learning algorithms for PCs often generate tree-structured circuits, or using tree-structured circuits as intermediate steps to compress them into DAG-structured circuits. This leads to an intriguing question on whether there exists an exponential gap between DAGs and trees for the PC structure.
In this paper, we provide a negative answer to this conjecture by proving that, for $n$ variables, there is a quasi-polynomial upper bound $n^{O(\log n)}$ on the size of an equivalent tree computing the same probability distribution. On the other hand, we will also show that given a depth restriction on the tree, there is a super-polynomial separation between tree and DAG-structured PCs. Our work takes an important step towards understanding the expressive power of tree-structured PCs, and our techniques may be of independent interest in the study of structure learning algorithms for PCs. | https://openreview.net/pdf/b43ed6380196199080503de5e5cbac4b6464df1a.pdf |
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data | https://openreview.net/forum?id=t9gNEhreht | https://openreview.net/forum?id=t9gNEhreht | Jialu Li,Jaemin Cho,Yi-Lin Sung,Jaehong Yoon,Mohit Bansal | NIPS 2024,Poster | Recent text-to-image (T2I) generation models have demonstrated impressive capabilities in creating images from text descriptions. However, these T2I generation models often fail to generate images that precisely match the details of the text inputs, such as incorrect spatial relationship or missing objects. In this paper, we introduce SELMA: Skill-Specific Expert Learning and Merging with Auto-Generated Data, a novel paradigm to improve the faithfulness of T2I models by fine-tuning models on automatically generated, multi-skill image-text datasets, with skill-specific expert learning and merging. First, SELMA leverages an LLM’s in-context learning capability to generate multiple datasets of text prompts that can teach different skills, and then generates the images with a T2I model based on the prompts. Next, SELMA adapts the T2I model to the new skills by learning multiple single-skill LoRA (low-rank adaptation) experts followed by expert merging. Our independent expert fine-tuning specializes multiple models for different skills, and expert merging helps build a joint multi-skill T2I model that can generate faithful images given diverse text prompts, while mitigating the knowledge conflict from different datasets. We empirically demonstrate that SELMA significantly improves the semantic alignment and text faithfulness of state-of-the-art T2I diffusion models on multiple benchmarks (+2.1% on TIFA and +6.9% on DSG), human preference metrics (PickScore, ImageReward, and HPS), as well as human evaluation. Moreover, fine-tuning with image-text pairs auto-collected via SELMA shows comparable performance to fine-tuning with ground truth data. Lastly, we show that fine-tuning with images from a weaker T2I model can help improve the generation quality of a stronger T2I model, suggesting promising weak-to-strong generalization in T2I models. We provide code in the supplementary materials. | https://openreview.net/pdf/9c5145da5a9316d95276533fdb05fffd3d83b851.pdf |
Thought of Search: Planning with Language Models Through The Lens of Efficiency | https://openreview.net/forum?id=lNCsyA5uS1 | https://openreview.net/forum?id=lNCsyA5uS1 | Michael Katz,Harsha Kokel,Kavitha Srinivas,Shirin Sohrabi | NIPS 2024,Poster | Among the most important properties of algorithms investigated in computer science are soundness, completeness, and complexity. These properties, however, are rarely analyzed for the vast collection of recently proposed methods for planning with large language models. In this work, we alleviate this gap. We analyse these properties of using LLMs for planning and highlight that recent trends abandon both soundness and completeness for the sake of inefficiency. We propose a significantly more efficient approach that can, at the same time, maintain both soundness and completeness. We exemplify on four representative search problems, comparing to the LLM-based solutions from the literature that attempt to solve these problems. We show that by using LLMs to produce the code for the search components we can solve the entire datasets with 100% accuracy with only a few calls to the LLM. In contrast, the compared approaches require hundreds of thousands of calls and achieve significantly lower accuracy. We argue for a responsible use of compute resources; urging research community to investigate sound and complete LLM-based approaches that uphold efficiency. | https://openreview.net/pdf/c3b9f6dac697975151973b9512513649e4a3cf31.pdf |
Universal Rates of Empirical Risk Minimization | https://openreview.net/forum?id=6cWDg9t3z5 | https://openreview.net/forum?id=6cWDg9t3z5 | Steve Hanneke,Mingyue Xu | NIPS 2024,Poster | The well-known $\textit{empirical risk minimization}$ (ERM) principle is the basis of many widely used machine learning algorithms, and plays an essential role in the classical PAC theory. A common description of a learning algorithm's performance is its so-called “learning curve”, that is, the decay of the expected error as a function of the input sample size. As the PAC model fails to explain the behavior of learning curves, recent research has explored an alternative universal learning model and has ultimately revealed a distinction between optimal universal and uniform learning rates (Bousquet et al., 2021). However, a basic understanding of such differences with a particular focus on the ERM principle has yet to be developed.
In this paper, we consider the problem of universal learning by ERM in the realizable case and study the possible universal rates. Our main result is a fundamental $\textit{tetrachotomy}$: there are only four possible universal learning rates by ERM, namely, the learning curves of any concept class learnable by ERM decay either at $e^{-n}$, $1/n$, $\log{(n)}/n$, or arbitrarily slow rates. Moreover, we provide a complete characterization of which concept classes fall into each of these categories, via new complexity structures. We also develop new combinatorial dimensions which supply sharp asymptotically-valid constant factors for these rates, whenever possible. | https://openreview.net/pdf/e3ad3581485fc4c7af6a155c8c66508125865813.pdf |
Interventional Causal Discovery in a Mixture of DAGs | https://openreview.net/forum?id=mFrlCI8sov | https://openreview.net/forum?id=mFrlCI8sov | Burak Varıcı,Dmitriy A Katz,Dennis Wei,Prasanna Sattigeri,Ali Tajer | NIPS 2024,Poster | Causal interactions among a group of variables are often modeled by a single causal graph. In some domains, however, these interactions are best described by multiple co-existing causal graphs, e.g., in dynamical systems or genomics. This paper addresses the hitherto unknown role of interventions in learning causal interactions among variables governed by a mixture of causal systems, each modeled by one directed acyclic graph (DAG). Causal discovery from mixtures is fundamentally more challenging than single-DAG causal discovery. Two major difficulties stem from (i) an inherent uncertainty about the skeletons of the component DAGs that constitute the mixture and (ii) possibly cyclic relationships across these component DAGs. This paper addresses these challenges and aims to identify edges that exist in at least one component DAG of the mixture, referred to as the *true* edges. First, it establishes matching necessary and sufficient conditions on the size of interventions required to identify the true edges. Next, guided by the necessity results, an adaptive algorithm is designed that learns all true edges using ${\cal O}(n^2)$ interventions, where $n$ is the number of nodes. Remarkably, the size of the interventions is optimal if the underlying mixture model does not contain cycles across its components. More generally, the gap between the intervention size used by the algorithm and the optimal size is quantified. It is shown to be bounded by the *cyclic complexity number* of the mixture model, defined as the size of the minimal intervention that can break the cycles in the mixture, which is upper bounded by the number of cycles among the ancestors of a node. | https://openreview.net/pdf/1b479d170e7f3c5cbb35a67cb00b9fc4c4843848.pdf |
Spatio-Spectral Graph Neural Networks | https://openreview.net/forum?id=Cb3kcwYBgw | https://openreview.net/forum?id=Cb3kcwYBgw | Simon Geisler,Arthur Kosmala,Daniel Herbst,Stephan Günnemann | NIPS 2024,Poster | Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of *ℓ*-step MPGNNs are that their "receptive field" is typically limited to the *ℓ*-hop neighborhood of a node and that information exchange between distant nodes is limited by over-squashing. Motivated by these limitations, we propose *Spatio-Spectral Graph Neural Networks (S²GNNs)* – a new modeling paradigm for Graph Neural Networks (GNNs) that synergistically combines spatially and spectrally parametrized graph filters. Parameterizing filters partially in the frequency domain enables global yet efficient information propagation. We show that S²GNNs vanquish over-squashing and yield strictly tighter approximation-theoretic error bounds than MPGNNs. Further, rethinking graph convolutions at a fundamental level unlocks new design spaces. For example, S²GNNs allow for free positional encodings that make them strictly more expressive than the 1-Weisfeiler-Leman (WL) test. Moreover, to obtain general-purpose S²GNNs, we propose spectrally parametrized filters for directed graphs. S²GNNs outperform spatial MPGNNs, graph transformers, and graph rewirings, e.g., on the peptide long-range benchmark tasks, and are competitive with state-of-the-art sequence modeling. On a 40 GB GPU, S²GNNs scale to millions of nodes. | https://openreview.net/pdf/3a46231cf042909d550605fa8da48926f0cf53e3.pdf |
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models | https://openreview.net/forum?id=FmNoFIImZG | https://openreview.net/forum?id=FmNoFIImZG | Andrei Margeloiu,Xiangjian Jiang,Nikola Simidjievski,Mateja Jamnik | NIPS 2024,Poster | Data collection is often difficult in critical fields such as medicine, physics, and chemistry, yielding typically only small tabular datasets. However, classification methods tend to struggle with these small datasets, leading to poor predictive performance. Increasing the training set with additional synthetic data, similar to data augmentation in images, is commonly believed to improve downstream tabular classification performance. However, current tabular generative methods that learn either the joint distribution $ p(\mathbf{x}, y) $ or the class-conditional distribution $ p(\mathbf{x} \mid y) $ often overfit on small datasets, resulting in poor-quality synthetic data, usually worsening classification performance compared to using real data alone. To solve these challenges, we introduce TabEBM, a novel class-conditional generative method using Energy-Based Models (EBMs). Unlike existing tabular methods that use a shared model to approximate all class-conditional densities, our key innovation is to create distinct EBM generative models for each class, each modelling its class-specific data distribution individually. This approach creates robust energy landscapes, even in ambiguous class distributions. Our experiments show that TabEBM generates synthetic data with higher quality and better statistical fidelity than existing methods. When used for data augmentation, our synthetic data consistently leads to improved classification performance across diverse datasets of various sizes, especially small ones. Code is available at https://github.com/andreimargeloiu/TabEBM. | https://openreview.net/pdf/25a6e595799705e000238621240dbbb44f3cf7d7.pdf |
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios | https://openreview.net/forum?id=uoJQ9qadjY | https://openreview.net/forum?id=uoJQ9qadjY | Shantanu Jaiswal,Debaditya Roy,Basura Fernando,Cheston Tan | NIPS 2024,Poster | Complex visual reasoning and question answering (VQA) is a challenging task that requires compositional multi-step processing and higher-level reasoning capabilities beyond the immediate recognition and localization of objects and events. Here, we introduce a fully neural Iterative and Parallel Reasoning Mechanism (IPRM) that combines two distinct forms of computation -- iterative and parallel -- to better address complex VQA scenarios. Specifically, IPRM's "iterative" computation facilitates compositional step-by-step reasoning for scenarios wherein individual operations need to be computed, stored, and recalled dynamically (e.g. when computing the query “determine the color of pen to the left of the child in red t-shirt sitting at the white table”). Meanwhile, its "parallel'' computation allows for the simultaneous exploration of different reasoning paths and benefits more robust and efficient execution of operations that are mutually independent (e.g. when counting individual colors for the query: "determine the maximum occurring color amongst all t-shirts'"). We design IPRM as a lightweight and fully-differentiable neural module that can be conveniently applied to both transformer and non-transformer vision-language backbones. It notably outperforms prior task-specific methods and transformer-based attention modules across various image and video VQA benchmarks testing distinct complex reasoning capabilities such as compositional spatiotemporal reasoning (AGQA), situational reasoning (STAR), multi-hop reasoning generalization (CLEVR-Humans) and causal event linking (CLEVRER-Humans). Further, IPRM's internal computations can be visualized across reasoning steps, aiding interpretability and diagnosis of its errors. | https://openreview.net/pdf/c993bb7e15acae1c52ff8d50243206913deb4cc9.pdf |
Zero-Shot Transfer of Neural ODEs | https://openreview.net/forum?id=OgnYoIxtIN | https://openreview.net/forum?id=OgnYoIxtIN | Tyler Ingebrand,Adam Thorpe,ufuk topcu | NIPS 2024,Poster | Autonomous systems often encounter environments and scenarios beyond the scope of their training data, which underscores a critical challenge: the need to generalize and adapt to unseen scenarios in real time. This challenge necessitates new mathematical and algorithmic tools that enable adaptation and zero-shot transfer. To this end, we leverage the theory of function encoders, which enables zero-shot transfer by combining the flexibility of neural networks with the mathematical principles of Hilbert spaces. Using this theory, we first present a method for learning a space of dynamics spanned by a set of neural ODE basis functions. After training, the proposed approach can rapidly identify dynamics in the learned space using an efficient inner product calculation. Critically, this calculation requires no gradient calculations or retraining during the online phase. This method enables zero-shot transfer for autonomous systems at runtime and opens the door for a new class of adaptable control algorithms. We demonstrate state-of-the-art system modeling accuracy for two MuJoCo robot environments and show that the learned models can be used for more efficient MPC control of a quadrotor. | https://openreview.net/pdf/6a650a5c71241b227459d9edda79c36d9a8fac28.pdf |
Graph neural networks and non-commuting operators | https://openreview.net/forum?id=6aJrEC28hR | https://openreview.net/forum?id=6aJrEC28hR | Mauricio Velasco,Kaiying O'Hare,Bernardo Rychtenberg,Soledad Villar | NIPS 2024,Poster | Graph neural networks (GNNs) provide state-of-the-art results in a wide variety of tasks which typically involve predicting features at the vertices of a graph. They are built from layers of graph convolutions which serve as a powerful inductive bias for describing the flow of information among the vertices. Often, more than one data modality is available. This work considers a setting in which several graphs have the same vertex set and a common vertex-level learning task. This generalizes standard GNN models to GNNs with several graph operators that do not commute. We may call this model graph-tuple neural networks (GtNN).
In this work, we develop the mathematical theory to address the stability and transferability of GtNNs using properties of non-commuting non-expansive operators. We develop a limit theory of graphon-tuple neural networks and use it to prove a universal transferability theorem that guarantees that all graph-tuple neural networks are transferable on convergent graph-tuple sequences. In particular, there is no non-transferable energy under the convergence we consider here. Our theoretical results extend well-known transferability theorems for GNNs to the case of several simultaneous graphs (GtNNs) and provide a strict improvement on what is currently known even in the GNN case.
We illustrate our theoretical results with simple experiments on synthetic and real-world data. To this end, we derive a training procedure that provably enforces the stability of the resulting model. | https://openreview.net/pdf/c7c518f298e5899f7e9285c55ca3425a0e7104ce.pdf |
Learning Goal-Conditioned Representations for Language Reward Models | https://openreview.net/forum?id=Swh8LxuycA | https://openreview.net/forum?id=Swh8LxuycA | Vaskar Nath,Dylan Z Slack,Jeff Da,Yuntao Ma,Hugh Zhang,Spencer Whitehead,Sean M. Hendryx | NIPS 2024,Poster | Techniques that learn improved representations via offline data or self-supervised objectives have shown impressive results in traditional reinforcement learning.
Nevertheless, it is unclear how improved representation learning can benefit reinforcement learning from human feedback on language models.
In this work, we propose training reward models (RMs) in a contrastive, $\textit{goal-conditioned}$ fashion by increasing the representation similarity of future states along sampled preferred trajectories and decreasing the similarity along randomly sampled dispreferred trajectories.
This objective significantly improves reward model performance by up to 0.09 AUROC across challenging benchmarks, such as MATH and GSM8k. These findings extend to general alignment as well -- on the Helpful-Harmless dataset, we observe 2.3\% increase in accuracy.
Beyond improving reward model performance, we show this way of training RM representations enables improved steerability because it allows us to evaluate the likelihood of an action achieving a particular goal-state (e.g. whether a solution is correct or helpful).
Leveraging this insight, we find that we can filter up to 55\% of generated tokens during majority voting by discarding trajectories likely to end up in an "incorrect" state, which leads to significant cost savings.
We additionally find that these representations can perform fine-grained control by conditioning on desired future goal-states.
For example, we show that steering a Llama 3 model towards helpful generations with our approach improves helpfulness by $9.6$\% over a supervised-fine-tuning trained baseline.
Similarly, steering the model towards complex generations improves complexity by $21.6$\% over the baseline.
Overall, we find that training RMs in this contrastive, goal-conditioned fashion significantly improves performance and enables model steerability. | https://openreview.net/pdf/83089f8621985bae77c25ae6a4e253c93dc4c5b0.pdf |
Rethinking Optimal Transport in Offline Reinforcement Learning | https://openreview.net/forum?id=hKloKv7pR2 | https://openreview.net/forum?id=hKloKv7pR2 | Arip Asadulaev,Rostislav Korst,Alexander Korotin,Vage Egiazarian,Andrey Filchenkov,Evgeny Burnaev | NIPS 2024,Poster | We propose a novel algorithm for offline reinforcement learning using optimal transport. Typically, in offline reinforcement learning, the data is provided by various experts and some of them can be sub-optimal. To extract an efficient policy, it is necessary to \emph{stitch} the best behaviors from the dataset. To address this problem, we rethink offline reinforcement learning as an optimal transportation problem. And based on this, we present an algorithm that aims to find a policy that maps states to a \emph{partial} distribution of the best expert actions for each given state. We evaluate the performance of our algorithm on continuous control problems from the D4RL suite and demonstrate improvements over existing methods. | https://openreview.net/pdf/6850154a5d5c4752d3397faaf8db1c686cc0f0c7.pdf |
Learning Identifiable Factorized Causal Representations of Cellular Responses | https://openreview.net/forum?id=AhlaBDHMQh | https://openreview.net/forum?id=AhlaBDHMQh | Haiyi Mao,Romain Lopez,Kai Liu,Jan-Christian Huetter,David Richmond,Panayiotis V. Benos,Lin Qiu | NIPS 2024,Poster | The study of cells and their responses to genetic or chemical perturbations promises to accelerate the discovery of therapeutics targets. However, designing adequate and insightful models for such data is difficult because the response of a cell to perturbations essentially depends on contextual covariates (e.g., genetic background or type of the cell). There is therefore a need for models that can identify interactions between drugs and contextual covariates. This is crucial for discovering therapeutics targets, as such interactions may reveal drugs that affect certain cell types but not others.
We tackle this problem with a novel Factorized Causal Representation (FCR) learning method, an identifiable deep generative model that reveals causal structure in single-cell perturbation data from several cell lines. FCR learns multiple cellular representations that are disentangled, comprised of covariate-specific (Z_x), treatment-specific (Z_t) and interaction-specific (Z_tx) representations. Based on recent advances of non-linear ICA theory, we prove the component-wise identifiability of Z_tx and block-wise identifiability of Z_t and Z_x. Then, we present our implementation of FCR, and empirically demonstrate that FCR outperforms state-of-the-art baselines in various tasks across four single-cell datasets. | https://openreview.net/pdf/4cd13872900e5194675e0f652bb052c6decdaffc.pdf |
Improved Sample Complexity Bounds for Diffusion Model Training | https://openreview.net/forum?id=OxcqkYOy8q | https://openreview.net/forum?id=OxcqkYOy8q | Shivam Gupta,Aditya Parulekar,Eric Price,Zhiyang Xun | NIPS 2024,Poster | Diffusion models have become the most popular approach to deep generative modeling of images, largely due to their empirical performance and reliability. From a theoretical standpoint, a number of recent works [CCL+23, CCSW22, BBDD24] have studied the iteration complexity of sampling, assuming access to an accurate diffusion model. In this work, we focus on understanding the *sample complexity* of training such a model; how many samples are needed to learn an accurate diffusion model using a sufficiently expressive neural network? Prior work [BMR20] showed bounds polynomial in the dimension, desired Total Variation error, and Wasserstein error. We show an *exponential improvement* in the dependence on Wasserstein error and depth, along with improved dependencies on other relevant parameters. | https://openreview.net/pdf/d9933ee6c6aa89329e716f365afa61a8a4b32bf0.pdf |
DiffusionPDE: Generative PDE-Solving under Partial Observation | https://openreview.net/forum?id=z0I2SbjN0R | https://openreview.net/forum?id=z0I2SbjN0R | Jiahe Huang,Guandao Yang,Zichen Wang,Jeong Joon Park | NIPS 2024,Poster | We introduce a general framework for solving partial differential equations (PDEs) using generative diffusion models. In particular, we focus on the scenarios where we do not have the full knowledge of the scene necessary to apply classical solvers. Most existing forward or inverse PDE approaches perform poorly when the observations on the data or the underlying coefficients are incomplete, which is a common assumption for real-world measurements. In this work, we propose DiffusionPDE that can simultaneously fill in the missing information and solve a PDE by modeling the joint distribution of the solution and coefficient spaces. We show that the learned generative priors lead to a versatile framework for accurately solving a wide range of PDEs under partial observation, significantly outperforming the state-of-the-art methods for both forward and inverse directions. | https://openreview.net/pdf/d12cbf722d1e7501e11593285562cb5fb783d08a.pdf |
Understanding Transformer Reasoning Capabilities via Graph Algorithms | https://openreview.net/forum?id=AfzbDw6DSp | https://openreview.net/forum?id=AfzbDw6DSp | Clayton Sanford,Bahare Fatemi,Ethan Hall,Anton Tsitsulin,Mehran Kazemi,Jonathan Halcrow,Bryan Perozzi,Vahab Mirrokni | NIPS 2024,Poster | Which transformer scaling regimes are able to perfectly solve different classes of algorithmic problems? While tremendous empirical advances have been attained by transformer-based neural networks, a theoretical understanding of their algorithmic reasoning capabilities in realistic parameter regimes is lacking. We investigate this question in terms of the network’s depth, width, and number of extra tokens for algorithm execution. Our novel representational hierarchy separates 9 algorithmic reasoning problems into classes solvable by transformers in different realistic parameter scaling regimes. We prove that logarithmic depth is necessary and sufficient for tasks like graph connectivity, while single-layer transformers with small embedding dimensions can solve contextual retrieval tasks. We also support our theoretical analysis with ample empirical evidence using the GraphQA benchmark. These results show that transformers excel at many graph reasoning tasks, even outperforming specialized graph neural networks. | https://openreview.net/pdf/d7fefe7e015fce36140124dc24c77636ac89cd5c.pdf |
Mutual Information Estimation via $f$-Divergence and Data Derangements | https://openreview.net/forum?id=PThi9hf9UT | https://openreview.net/forum?id=PThi9hf9UT | Nunzio Alexandro Letizia,Nicola Novello,Andrea M Tonello | NIPS 2024,Poster | Estimating mutual information accurately is pivotal across diverse applications, from machine learning to communications and biology, enabling us to gain insights into the inner mechanisms of complex systems. Yet, dealing with high-dimensional data presents a formidable challenge, due to its size and the presence of intricate relationships. Recently proposed neural methods employing variational lower bounds on the mutual information have gained prominence. However, these approaches suffer from either high bias or high variance, as the sample size and the structure of the loss function directly influence the training process. In this paper, we propose a novel class of discriminative mutual information estimators based on the variational representation of the $f$-divergence. We investigate the impact of the permutation function used to obtain the marginal training samples and present a novel architectural solution based on derangements. The proposed estimator is flexible since it exhibits an excellent bias/variance trade-off. The comparison with state-of-the-art neural estimators, through extensive experimentation within established reference scenarios, shows that our approach offers higher accuracy and lower complexity. | https://openreview.net/pdf/3501069f61bdb73e7304fe1435267c1eb2e285d2.pdf |
Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe | https://openreview.net/forum?id=kVL5rvkqGG | https://openreview.net/forum?id=kVL5rvkqGG | Albert Q. Jiang,Alicja Ziarko,Bartosz Piotrowski,Wenda Li,Mateja Jamnik,Piotr Miłoś | NIPS 2024,Poster | Text embeddings are essential for tasks such as document retrieval, clustering, and semantic similarity assessment. In this paper, we study how to contrastively train text embedding models in a compute-optimal fashion, given a suite of pretrained decoder-only language models. Our innovation is an algorithm that produces optimal configurations of model sizes, data quantities, and fine-tuning methods for text-embedding models at different computational budget levels. The resulting recipe, which we obtain through extensive experiments, can be used by practitioners to make informed design choices for their embedding models. Specifically, our findings suggest that full fine-tuning and Low-Rank Adaptation fine-tuning produce optimal models at lower and higher computational budgets respectively. | https://openreview.net/pdf/eefb08b5efe5e72f4d802bf9c78722becbd5a0f9.pdf |
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms | https://openreview.net/forum?id=pf4OuJyn4Q | https://openreview.net/forum?id=pf4OuJyn4Q | Rafael Rafailov,Yaswanth Chittepu,Ryan Park,Harshit Sikchi,Joey Hejna,W. Bradley Knox,Chelsea Finn,Scott Niekum | NIPS 2024,Poster | Reinforcement Learning from Human Feedback (RLHF)has been crucial to the recent success of Large Language Models (LLMs), however it is often a complex and brittle process. In the classical RLHF framework, a reward model is first trained to represent human preferences, which is in turn used by an online reinforcement learning (RL) algorithm to optimized the LLM. A prominent issue with such methods is reward over-optimization or reward hacking, where the performance as measured by the learned proxy reward model increases, but the true model quality plateaus or even deteriorates. Direct Alignment Algorithms (DDAs), such as Direct Preference Optimization (DPO) have emerged as alternatives to the classical RLHF pipeline. However, despite not training a separate proxy reward model or using RL, they still commonly deteriorate from over-optimization. While the so-called reward hacking phenomenon is not well-defined for DAAs, we still uncover similar trends: at higher KL-budgets, DAA algorithms exhibit similar degradation patters to their classic RLHF counterparts. In particular, we find that DAA methods deteriorate not only across a wide range of KL-budgets, but also often before even a single epoch of the dataset is completed. Through extensive empirical experimentation this work formulates the reward over-optimization or hacking problem for DAAs and explores its consequences across objectives, training regimes, and model scales. | https://openreview.net/pdf/e28365fb2dd2a9b94f8225bee790989091ef456f.pdf |
Mixture of Demonstrations for In-Context Learning | https://openreview.net/forum?id=uqxSLoCw3K | https://openreview.net/forum?id=uqxSLoCw3K | Song Wang,Zihan Chen,Chengshuai Shi,Cong Shen,Jundong Li | NIPS 2024,Poster | In-Context Learning (ICL) empowers Large Language Models (LLMs) to tackle various tasks by providing input-output examples as additional inputs, referred to as demonstrations. Nevertheless, the performance of ICL could be easily impacted by the quality of selected demonstrations. Existing efforts generally learn a retriever model to score each demonstration for selecting suitable demonstrations, however, the effect is suboptimal due to the large search space and the noise from unhelpful demonstrations. In this study, we introduce MoD, which partitions the demonstration pool into groups, each governed by an expert to reduce search space. We further design an expert-wise training strategy to alleviate the impact of unhelpful demonstrations when optimizing the retriever model. During inference, experts collaboratively retrieve demonstrations for the input query to enhance the ICL performance. We validate MoD via experiments across a range of NLP datasets and tasks, demonstrating its state-of-the-art performance and shedding new light on the future design of retrieval methods for ICL. | https://openreview.net/pdf/fc5db0bdacac2fe51883bcf90ff63fbac5bdbf0d.pdf |
VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks | https://openreview.net/forum?id=kuCY0mW4Q3 | https://openreview.net/forum?id=kuCY0mW4Q3 | Yang Li,Shaobo Han,Shihao Ji | NIPS 2024,Poster | As the adoption of large language models increases and the need for per-user or per-task model customization grows, the parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and its variants, incur substantial storage and transmission costs. To further reduce stored parameters, we introduce a "divide-and-share" paradigm that breaks the barriers of low-rank decomposition across matrix dimensions, modules, and layers by sharing parameters globally via a vector bank. As an instantiation of the paradigm to LoRA, our proposed VB-LoRA composites all the low-rank matrices of LoRA from a shared vector bank with a differentiable top-$k$ admixture module. VB-LoRA achieves extreme parameter efficiency while maintaining comparable or better performance compared to state-of-the-art PEFT methods. Extensive experiments demonstrate the effectiveness of VB-LoRA on natural language understanding, natural language generation, instruction tuning, and mathematical reasoning tasks. When fine-tuning the Llama2-13B model, VB-LoRA only uses 0.4% of LoRA's stored parameters, yet achieves superior results. Our source code is available at https://github.com/leo-yangli/VB-LoRA. This method has been merged into the Hugging Face PEFT package. | https://openreview.net/pdf/d0ded460792ec7f1fc26f1bf560cf85baa3118db.pdf |
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning | https://openreview.net/forum?id=PLbFid00aU | https://openreview.net/forum?id=PLbFid00aU | Michael Munn,Benoit Dherin,Javier Gonzalvo | NIPS 2024,Poster | Many of the recent advances in computer vision and language models can be attributed to the success of transfer learning via the pre-training of large foundation models. However, a theoretical framework which explains this empirical success is incomplete and remains an active area of research. Flatness of the loss surface and neural collapse have recently emerged as useful pre-training metrics which shed light on the implicit biases underlying pre-training. In this paper, we explore the geometric complexity of a model's learned representations as a fundamental mechanism that relates these two concepts. We show through experiments and theory that mechanisms which affect the geometric complexity of the pre-trained network also influence the neural collapse. Furthermore, we show how this effect of the geometric complexity generalizes to the neural collapse of new classes as well, thus encouraging better performance on downstream tasks, particularly in the few-shot setting. | https://openreview.net/pdf/4a8f5a51cf4f2f62fae15a4d92e58f25651664b5.pdf |
Fine-Tuning is Fine, if Calibrated | https://openreview.net/forum?id=XRJXKBeeTD | https://openreview.net/forum?id=XRJXKBeeTD | Zheda Mai,Arpita Chowdhury,Ping Zhang,Cheng-Hao Tu,Hong-You Chen,Vardaan Pahuja,Tanya Berger-Wolf,Song Gao,Charles Stewart,Yu Su,Wei-Lun Chao | NIPS 2024,Poster | Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing valuable knowledge the model had learned in pre-training. For example, fine-tuning a pre-trained classifier capable of recognizing a large number of classes to master a subset of classes at hand is shown to drastically degrade the model's accuracy in the other classes it had previously learned. As such, it is hard to further use the fine-tuned model when it encounters classes beyond the fine-tuning data. In this paper, we systematically dissect the issue, aiming to answer the fundamental question, "What has been damaged in the fine-tuned model?" To our surprise, we find that the fine-tuned model neither forgets the relationship among the other classes nor degrades the features to recognize these classes. Instead, the fine-tuned model often produces more discriminative features for these other classes, even if they were missing during fine-tuning! What really hurts the accuracy is the discrepant logit scales between the fine-tuning classes and the other classes, implying that a simple post-processing calibration would bring back the pre-trained model's capability and at the same time unveil the feature improvement over all classes. We conduct an extensive empirical study to demonstrate the robustness of our findings and provide preliminary explanations underlying them, suggesting new directions for future theoretical analysis. | https://openreview.net/pdf/03216393b8d5442283cc9fa69b62f9c7a7120339.pdf |
Adversarially Robust Decision Transformer | https://openreview.net/forum?id=WEf2LT8NtY | https://openreview.net/forum?id=WEf2LT8NtY | Xiaohang Tang,Afonso Marques,Parameswaran Kamalaruban,Ilija Bogunovic | NIPS 2024,Poster | Decision Transformer (DT), as one of the representative Reinforcement Learning via Supervised Learning (RvS) methods, has achieved strong performance in offline learning tasks by leveraging the powerful Transformer architecture for sequential decision-making. However, in adversarial environments, these methods can be non-robust, since the return is dependent on the strategies of both the decision-maker and adversary. Training a probabilistic model conditioned on observed return to predict action can fail to generalize, as the trajectories that achieve a return in the dataset might have done so due to a suboptimal behavior adversary. To address this, we propose a worst-case-aware RvS algorithm, the Adversarially Robust Decision Transformer (ARDT), which learns and conditions the policy on in-sample minimax returns-to-go.
ARDT aligns the target return with the worst-case return learned through minimax expectile regression, thereby enhancing robustness against powerful test-time adversaries. In experiments conducted on sequential games with full data coverage, ARDT can generate a maximin (Nash Equilibrium) strategy, the solution with the largest adversarial robustness. In large-scale sequential games and continuous adversarial RL environments with partial data coverage, ARDT demonstrates significantly superior robustness to powerful test-time adversaries and attains higher worst-case returns compared to contemporary DT methods. | https://openreview.net/pdf/1b9376570681f65dffb0184cd4e6ab76cdc18367.pdf |
User-Creator Feature Polarization in Recommender Systems with Dual Influence | https://openreview.net/forum?id=yWq89o19wf | https://openreview.net/forum?id=yWq89o19wf | Tao Lin,Kun Jin,Andrew Estornell,Xiaoying Zhang,Yiling Chen,Yang Liu | NIPS 2024,Poster | Recommender systems serve the dual purpose of presenting relevant content to users and helping content creators reach their target audience. The dual nature of these systems naturally influences both users and creators: users' preferences are affected by the items they are recommended, while creators may be incentivized to alter their content to attract more users. We define a model, called user-creator feature dynamics, to capture the dual influence of recommender systems. We prove that a recommender system with dual influence is guaranteed to polarize, causing diversity loss in the system. We then investigate, both theoretically and empirically, approaches for mitigating polarization and promoting diversity in recommender systems. Unexpectedly, we find that common diversity-promoting approaches do not work in the presence of dual influence, while relevancy-optimizing methods like top-$k$ truncation can prevent polarization and improve diversity of the system. | https://openreview.net/pdf/85ebe96bf9fedbc2fcdde28d66e0c8df3b4c3061.pdf |
LoQT: Low-Rank Adapters for Quantized Pretraining | https://openreview.net/forum?id=Pnv8C0bU9t | https://openreview.net/forum?id=Pnv8C0bU9t | Sebastian Bugge Loeschcke,Mads Toftrup,Michael Kastoryano,Serge Belongie,Vésteinn Snæbjarnarson | NIPS 2024,Poster | Despite advances using low-rank adapters and quantization, pretraining of large models on consumer hardware has not been possible without model sharding, offloading during training, or per-layer gradient updates. To address these limitations, we propose Low-Rank Adapters for Quantized Training (LoQT), a method for efficiently training quantized models. LoQT uses gradient-based tensor factorization to initialize low-rank trainable weight matrices that are periodically merged into quantized full-rank weight matrices. Our approach is suitable for both pretraining and fine-tuning models. We demonstrate this for language modeling and downstream task adaptation, finding that LoQT enables efficient training of models up to 7B parameters on a 24GB GPU. We also demonstrate the feasibility of training a 13B model using per-layer gradient updates on the same hardware. | https://openreview.net/pdf/d532678cf4637d7d315ebd723285c6b6b58529b1.pdf |
Unifying Generation and Prediction on Graphs with Latent Graph Diffusion | https://openreview.net/forum?id=lvibangnAs | https://openreview.net/forum?id=lvibangnAs | Cai Zhou,Xiyuan Wang,Muhan Zhang | NIPS 2024,Poster | In this paper, we propose the first framework that enables solving graph learning tasks of all levels (node, edge and graph) and all types (generation, regression and classification) using one formulation. We first formulate prediction tasks including regression and classification into a generic (conditional) generation framework, which enables diffusion models to perform deterministic tasks with provable guarantees. We then propose Latent Graph Diffusion (LGD), a generative model that can generate node, edge, and graph-level features of all categories simultaneously. We achieve this goal by embedding the graph structures and features into a latent space leveraging a powerful encoder and decoder, then training a diffusion model in the latent space. LGD is also capable of conditional generation through a specifically designed cross-attention mechanism. Leveraging LGD and the ``all tasks as generation'' formulation, our framework is capable of solving graph tasks of various levels and types. We verify the effectiveness of our framework with extensive experiments, where our models achieve state-of-the-art or highly competitive results across a wide range of generation and regression tasks. | https://openreview.net/pdf/a4a43894359b12777ca40a61fe02fe738116f751.pdf |
On the Role of Attention Masks and LayerNorm in Transformers | https://openreview.net/forum?id=lIH6oCdppg | https://openreview.net/forum?id=lIH6oCdppg | Xinyi Wu,Amir Ajorlou,Yifei Wang,Stefanie Jegelka,Ali Jadbabaie | NIPS 2024,Poster | Self-attention is the key mechanism of transformers, which are the essential building blocks of modern foundation models. Recent studies have shown that pure self-attention suffers from an increasing degree of rank collapse as depth increases, limiting model expressivity and further utilization of model depth. The existing literature on rank collapse, however, has mostly overlooked other critical components in transformers that may alleviate the rank collapse issue. In this paper, we provide a general analysis of rank collapse under self-attention, taking into account the effects of attention masks and layer normalization (LayerNorm). In particular, we find that although pure masked attention still suffers from exponential collapse to a rank one subspace, sparse or local masked attention can provably slow down the collapse rate. In the case of self-attention with LayerNorm, we first show that for certain classes of value matrices, collapse to a rank one subspace still happens exponentially. However, through construction of nontrivial counterexamples, we then establish that with proper choice of value matrices, a general class of sequences may not converge to a rank one subspace, and the self-attention dynamics with LayerNorm can simultaneously possess a rich set of equilibria with any possible rank between one and full. Our result refutes the previous hypothesis that LayerNorm plays no role in the rank collapse of self-attention and suggests that self-attention with LayerNorm constitutes a much more expressive, versatile nonlinear dynamical system than what was originally thought. | https://openreview.net/pdf/e3f16b5718fda36c132a629e2933050ff3bab9e7.pdf |
Grammar-Aligned Decoding | https://openreview.net/forum?id=5G7ve8E1Lu | https://openreview.net/forum?id=5G7ve8E1Lu | Kanghee Park,Jiayu Wang,Taylor Berg-Kirkpatrick,Nadia Polikarpova,Loris D'Antoni | NIPS 2024,Poster | Large Language Models (LLMs) struggle with reliably generating highly structured outputs, such as program code, mathematical formulas, or well-formed markup. Constrained decoding approaches mitigate this problem by greedily restricting what tokens an LLM can output at each step to guarantee that the output matches a given constraint. Specifically, in grammar-constrained decoding (GCD), the LLM's output must follow a given grammar. In this paper we demonstrate that GCD techniques (and in general constrained decoding techniques) can distort the LLM's distribution, leading to outputs that are grammatical but appear with likelihoods that are not proportional to the ones given by the LLM, and so ultimately are low-quality. We call the problem of aligning sampling with a grammar constraint, grammar-aligned decoding (GAD), and propose adaptive sampling with approximate expected futures (ASAp), a decoding algorithm that guarantees the output to be grammatical while provably producing outputs that match the conditional probability of the LLM's distribution conditioned on the given grammar constraint. Our algorithm uses prior sample outputs to soundly overapproximate the future grammaticality of different output prefixes. Our evaluation on code generation and structured NLP tasks shows how ASAp often produces outputs with higher likelihood (according to the LLM's distribution) than existing GCD techniques, while still enforcing the desired grammatical constraints. | https://openreview.net/pdf/504817cfb495898362dd26d0e8f8d704bfc7e323.pdf |
Symmetry-Informed Governing Equation Discovery | https://openreview.net/forum?id=aeGSA8UoXF | https://openreview.net/forum?id=aeGSA8UoXF | Jianke Yang,Wang Rao,Nima Dehmamy,Robin Walters,Rose Yu | NIPS 2024,Poster | Despite the advancements in learning governing differential equations from observations of dynamical systems, data-driven methods are often unaware of fundamental physical laws, such as frame invariance. As a result, these algorithms may search an unnecessarily large space and discover less accurate or overly complex equations. In this paper, we propose to leverage symmetry in automated equation discovery to compress the equation search space and improve the accuracy and simplicity of the learned equations. Specifically, we derive equivariance constraints from the time-independent symmetries of ODEs. Depending on the types of symmetries, we develop a pipeline for incorporating symmetry constraints into various equation discovery algorithms, including sparse regression and genetic programming. In experiments across diverse dynamical systems, our approach demonstrates better robustness against noise and recovers governing equations with significantly higher probability than baselines without symmetry. | https://openreview.net/pdf/250a0df869100c633849c08a12d81753505c0f07.pdf |
Robust Conformal Prediction Using Privileged Information | https://openreview.net/forum?id=kkmPe0rzY1 | https://openreview.net/forum?id=kkmPe0rzY1 | Shai Feldman,Yaniv Romano | NIPS 2024,Poster | We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables.
Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption. Importantly, naively applying conformal prediction does not provide reliable predictions in this setting, due to the distribution shift induced by the corruptions.
To account for the distribution shift, we assume access to privileged information (PI). The PI is formulated as additional features that explain the distribution shift, however, they are only available during training and absent at test time.
We approach this problem by introducing a novel generalization of weighted conformal prediction and support our method with theoretical coverage guarantees.
Empirical experiments on both real and synthetic datasets indicate that our approach achieves a valid coverage rate and constructs more informative predictions compared to existing methods, which are not supported by theoretical guarantees. | https://openreview.net/pdf/17e2a30dc421d29baba4305ca20cf807ff7774e7.pdf |
Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism | https://openreview.net/forum?id=He2GCHeRML | https://openreview.net/forum?id=He2GCHeRML | Ronast Subedi,Lu Wei,Wenhan Gao,Shayok Chakraborty,Yi Liu | NIPS 2024,Poster | Molecular learning is pivotal in many real-world applications, such as drug discovery. Supervised learning requires heavy human annotation, which is particularly challenging for molecular data, e.g., the commonly used density functional theory (DFT) is highly computationally expensive. Active learning (AL) automatically queries labels for most informative samples, thereby remarkably alleviating the annotation hurdle. In this paper, we present a principled AL paradigm for molecular learning, where we treat molecules as 3D molecular graphs. Specifically, we propose a new diversity sampling method to eliminate mutual redundancy built on distributions of 3D geometries. We first propose a set of new 3D graph isometries for 3D graph isomorphism analysis. Our method is provably at least as expressive as the Geometric Weisfeiler-Lehman (GWL) test. The moments of the distributions of the associated geometries are then extracted for efficient diversity computing. To ensure our AL paradigm selects samples with maximal uncertainties, we carefully design a Bayesian geometric graph neural network to compute uncertainties specifically for 3D molecular graphs. We pose active sampling as a quadratic programming (QP) problem using the proposed components. Experimental results demonstrate the effectiveness of our AL paradigm, as well as the proposed diversity and uncertainty methods. | https://openreview.net/pdf/55839f775cb921be502c4ab77345db777189a822.pdf |
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening | https://openreview.net/forum?id=9cFyqhjEHC | https://openreview.net/forum?id=9cFyqhjEHC | Guy Bar-Shalom,Yam Eitan,Fabrizio Frasca,Haggai Maron | NIPS 2024,Poster | Subgraph GNNs enhance message-passing GNNs expressivity by representing graphs as sets of subgraphs, demonstrating impressive performance across various tasks. However, their scalability is hindered by the need to process large numbers of subgraphs. While previous approaches attempted to generate smaller subsets of subgraphs through random or learnable sampling, these methods often yielded suboptimal selections or were limited to small subset sizes, ultimately compromising their effectiveness. This paper introduces a new Subgraph GNN framework to address these issues.
Our approach diverges from most previous methods by associating subgraphs with node clusters rather than with individual nodes. We show that the resulting collection of subgraphs can be viewed as the product of coarsened and original graphs, unveiling a new connectivity structure on which we perform generalized message passing.
Crucially, controlling the coarsening function enables meaningful selection of any number of subgraphs. In addition, we reveal novel permutation symmetries in the resulting node feature tensor, characterize associated linear equivariant layers, and integrate them into our Subgraph GNN. We also introduce novel node marking strategies and provide a theoretical analysis of their expressive power and other key aspects of our approach. Extensive experiments on multiple graph learning benchmarks demonstrate that our method is significantly more flexible than previous approaches, as it can seamlessly handle any number of subgraphs, while consistently outperforming baseline approaches.
Our code is available at https://github.com/BarSGuy/Efficient-Subgraph-GNNs. | https://openreview.net/pdf/f366866b89a021aabbbdbc564209bc7ac0008082.pdf |
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy | https://openreview.net/forum?id=YaPhvbGqwO | https://openreview.net/forum?id=YaPhvbGqwO | Cameron Allen,Aaron T. Kirtland,Ruo Yu Tao,Sam Lobel,Daniel Scott,Nicholas Petrocelli,Omer Gottesman,Ronald Parr,Michael Littman,George Konidaris | NIPS 2024,Poster | Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can an agent learn such a state representation, and how can it detect when it has found one? We introduce a metric that can accomplish both objectives, without requiring access to---or knowledge of---an underlying, unobservable state space. Our metric, the λ-discrepancy, is the difference between two distinct temporal difference (TD) value estimates, each computed using TD(λ) with a different value of λ. Since TD(λ=0) makes an implicit Markov assumption and TD(λ=1) does not, a discrepancy between these estimates is a potential indicator of a non-Markovian state representation. Indeed, we prove that the λ-discrepancy is exactly zero for all Markov decision processes and almost always non-zero for a broad class of partially observable environments. We also demonstrate empirically that, once detected, minimizing the λ-discrepancy can help with learning a memory function to mitigate the corresponding partial observability. We then train a reinforcement learning agent that simultaneously constructs two recurrent value networks with different λ parameters and minimizes the difference between them as an auxiliary loss. The approach scales to challenging partially observable domains, where the resulting agent frequently performs significantly better (and never performs worse) than a baseline recurrent agent with only a single value network. | https://openreview.net/pdf/677c00d620d79ecf3e441f1574c27cd2a075022d.pdf |
UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation | https://openreview.net/forum?id=luQiVmnviX | https://openreview.net/forum?id=luQiVmnviX | Hanzhang Zhou,Zijian Feng,Zixiao Zhu,Junlang Qian,Kezhi Mao | NIPS 2024,Poster | Large language models (LLMs) have demonstrated impressive capabilities in various tasks using the in-context learning (ICL) paradigm. However, their effectiveness is often compromised by inherent bias, leading to prompt brittleness—sensitivity to design settings such as example selection, order, and prompt formatting. Previous studies have addressed LLM bias through external adjustment of model outputs, but the internal mechanisms that lead to such bias remain unexplored. Our work delves into these mechanisms, particularly investigating how feedforward neural networks (FFNs) and attention heads result in the bias of LLMs. By Interpreting the contribution of individual FFN vectors and attention heads, we identify the biased LLM components that skew LLMs' prediction toward specific labels. To mitigate these biases, we introduce UniBias, an inference-only method that effectively identifies and eliminates biased FFN vectors and attention heads. Extensive experiments across 12 NLP datasets demonstrate that UniBias significantly enhances ICL performance and alleviates prompt brittleness of LLMs. | https://openreview.net/pdf/358d90ff7d8d269005fc19a00baabfed20752d30.pdf |
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems | https://openreview.net/forum?id=dCgbyvmlwL | https://openreview.net/forum?id=dCgbyvmlwL | Zhi Zheng,Changliang Zhou,Tong Xialiang,Mingxuan Yuan,Zhenkun Wang | NIPS 2024,Poster | Single-stage neural combinatorial optimization solvers have achieved near-optimal results on various small-scale combinatorial optimization (CO) problems without requiring expert knowledge. However, these solvers exhibit significant performance degradation when applied to large-scale CO problems. Recently, two-stage neural methods motivated by divide-and-conquer strategies have shown efficiency in addressing large-scale CO problems. Nevertheless, the performance of these methods highly relies on problem-specific heuristics in either the dividing or the conquering procedure, which limits their applicability to general CO problems. Moreover, these methods employ separate training schemes and ignore the interdependencies between the dividing and conquering strategies, often leading to sub-optimal solutions. To tackle these drawbacks, this article develops a unified neural divide-and-conquer framework (i.e., UDC) for solving general large-scale CO problems. UDC offers a Divide-Conquer-Reunion (DCR) training method to eliminate the negative impact of a sub-optimal dividing policy. Employing a high-efficiency Graph Neural Network (GNN) for global instance dividing and a fixed-length sub-path solver for conquering divided sub-problems, the proposed UDC framework demonstrates extensive applicability, achieving superior performance in 10 representative large-scale CO problems. The code is available at https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/UDC-Large-scale-CO-master | https://openreview.net/pdf/62b7143496cc33a5dbe9b13c98094f20b2043fc5.pdf |
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction | https://openreview.net/forum?id=zBMKodNgKX | https://openreview.net/forum?id=zBMKodNgKX | Ziwei Li,Xiaoqi Wang,Hong-You Chen,Han Wei Shen,Wei-Lun Chao | NIPS 2024,Poster | Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. Despite its broad applications in fields such as computer vision, graph learning, and natural language processing, the development of a data projection model that can be effectively used to visualize data in the context of FL is crucial yet remains heavily under-explored. Neighbor embedding (NE) is an essential technique for visualizing complex high-dimensional data, but collaboratively learning a joint NE model is difficult. The key challenge lies in the objective function, as effective visualization algorithms like NE require computing loss functions among pairs of data.
In this paper, we introduce \textsc{FedNE}, a novel approach that integrates the \textsc{FedAvg} framework with the contrastive NE technique, without any requirements of shareable data. To address the lack of inter-client repulsion which is crucial for the alignment in the global embedding space, we develop a surrogate loss function that each client learns and shares with each other. Additionally, we propose a data-mixing strategy to augment the local data, aiming to relax the problems of invisible neighbors and false neighbors constructed by the local $k$NN graphs. We conduct comprehensive experiments on both synthetic and real-world datasets. The results demonstrate that our \textsc{FedNE} can effectively preserve the neighborhood data structures and enhance the alignment in the global embedding space compared to several baseline methods. | https://openreview.net/pdf/713ead3a7d3c84218a49bae4d46cdf7a3a34d042.pdf |
DisCEdit: Model Editing by Identifying Discriminative Components | https://openreview.net/forum?id=tuiqq1G8I5 | https://openreview.net/forum?id=tuiqq1G8I5 | Chaitanya Murti,Chiranjib Bhattacharyya | NIPS 2024,Poster | Model editing is a growing area of research that is particularly valuable in contexts where modifying key model components, like neurons or filters, can significantly impact the model’s performance. The key challenge lies in identifying important components useful to the model’s predictions. We apply model editing to address two active areas of research, Structured Pruning, and Selective Class Forgetting. In this work, we adopt a distributional approach to the problem of identifying important components, leveraging the recently proposed discriminative filters hypothesis, which states that well-trained (convolutional) models possess discriminative filters that are essential to prediction. To do so, we define discriminative ability in terms of the Bayes error rate associated with the feature distributions, which is equivalent to computing the Total Variation (TV) distance between the distributions. However, computing the TV distance is intractable, motivating us to derive novel witness function-based lower bounds on the TV distance that require no assumptions on the underlying distributions; using this bound generalizes prior work such as Murti et al. [39] that relied on unrealistic Gaussianity assumptions on the feature distributions. With these bounds, we are able to discover critical subnetworks responsible for classwise predictions, and derive DISCEDIT-SP and DISCEDIT-U , algorithms for structured pruning requiring no access to the training data and loss function, and selective forgetting respectively. We apply DISCEDIT-U to selective class forgetting on models trained on CIFAR10 and CIFAR100, and we show that on average, we can reduce accuracy on a single class by over 80% with a minimal reduction in test accuracy on the remaining classes. Similarly, on Structured pruning problems, we obtain 40.8% sparsity on ResNet50 on Imagenet, with only a 2.6% drop in accuracy with minimal fine-tuning. | https://openreview.net/pdf/dbd295af5e5b4cdccb26116ce210661a174d96e3.pdf |
Equivariant spatio-hemispherical networks for diffusion MRI deconvolution | https://openreview.net/forum?id=MxWpCherzD | https://openreview.net/forum?id=MxWpCherzD | Axel Elaldi,Guido Gerig,Neel Dey | NIPS 2024,Poster | Each voxel in a diffusion MRI (dMRI) image contains a spherical signal corresponding to the direction and strength of water diffusion in the brain. This paper advances the analysis of such spatio-spherical data by developing convolutional network layers that are equivariant to the $\mathbf{E(3) \times SO(3)}$ group and account for the physical symmetries of dMRI including rotations, translations, and reflections of space alongside voxel-wise rotations. Further, neuronal fibers are typically antipodally symmetric, a fact we leverage to construct highly efficient spatio-*hemispherical* graph convolutions to accelerate the analysis of high-dimensional dMRI data. In the context of sparse spherical fiber deconvolution to recover white matter microstructure, our proposed equivariant network layers yield substantial performance and efficiency gains, leading to better and more practical resolution of crossing neuronal fibers and fiber tractography. These gains are experimentally consistent across both simulation and in vivo human datasets. | https://openreview.net/pdf/07454cead061994ce354011bd5b90e9556232add.pdf |
SkipPredict: When to Invest in Predictions for Scheduling | https://openreview.net/forum?id=kVuw8vzsqZ | https://openreview.net/forum?id=kVuw8vzsqZ | Rana Shahout,Michael Mitzenmacher | NIPS 2024,Poster | Expanding on recent work on scheduling with predicted job sizes, we consider the effect of the cost of predictions in queueing systems, removing the assumption in prior research that predictions are external to the system’s resources and/or cost-free. Additionally, we introduce a novel approach to utilizing predictions, SkipPredict, designed to address their inherent cost. Rather than uniformly applying predictions to all jobs, we propose a tailored approach that categorizes jobs to improve the effectiveness of prediction on performance. To achieve this, we employ one-bit “cheap predictions” to classify jobs as either short or long. SkipPredict prioritizes predicted short jobs over long jobs, and for the long jobs, SkipPredict applies a second round of more detailed “expensive predictions” to approximate Shortest Remaining Processing Time for these jobs. Importantly, our analyses take into account the cost of prediction. We derive closed-form formulas that calculate the mean response time of jobs with size predictions accounting for the prediction cost. We examine the effect of this cost for two distinct models in real-world and synthetic datasets. In the external cost model, predictions are generated by external method without impacting job service times but incur a cost. In the server time cost model, predictions themselves require server processing time and are scheduled on the same server as the jobs. | https://openreview.net/pdf/5162380d9c32a0fd2737b61b998c536a3eec2ceb.pdf |
Deep Equilibrium Algorithmic Reasoning | https://openreview.net/forum?id=SuLxkxCENa | https://openreview.net/forum?id=SuLxkxCENa | Dobrik Georgiev Georgiev,JJ Wilson,Davide Buffelli,Pietro Lio | NIPS 2024,Poster | Neural Algorithmic Reasoning (NAR) research has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms. However, most previous approaches have always used a recurrent architecture, where each iteration of the GNN matches an iteration of the algorithm. In this paper we study neurally solving algorithms from a different perspective: since the algorithm’s solution is often an equilibrium, it is possible to find the solution directly by solving an equilibrium equation. Our approach requires no information on the ground-truth number of steps of the algorithm, both during train and test time. Furthermore, the proposed method improves the performance of GNNs on executing algorithms and is a step towards speeding up existing NAR models. Our empirical evidence, leveraging algorithms from the CLRS-30 benchmark, validates that one can train a network to solve algorithmic problems by directly finding the equilibrium. We discuss the practical implementation of such models and propose regularisations to improve the performance of these equilibrium reasoners. | https://openreview.net/pdf/8b85ffde2de0fe0a152c8cfa917ab32b850cee6b.pdf |
Soft-Label Integration for Robust Toxicity Classification | https://openreview.net/forum?id=iYkhThIXG1 | https://openreview.net/forum?id=iYkhThIXG1 | Zelei Cheng,Xian Wu,Jiahao Yu,Shuo Han,Xin-Qiang Cai,Xinyu Xing | NIPS 2024,Poster | Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations for training an effective toxicity classifier. Additionally, the standard approach to training a classifier using empirical risk minimization (ERM) may fail to address the potential shifts between the training set and testing set due to exploiting spurious correlations. This work introduces a novel bi-level optimization framework that integrates crowdsourced annotations with the soft-labeling technique and optimizes the soft-label weights by Group Distributionally Robust Optimization (GroupDRO) to enhance the robustness against out-of-distribution (OOD) risk. We theoretically prove the convergence of our bi-level optimization algorithm. Experimental results demonstrate that our approach outperforms existing baseline methods in terms of both average and worst-group accuracy, confirming its effectiveness in leveraging crowdsourced annotations to achieve more effective and robust toxicity classification. | https://openreview.net/pdf/57903154d4f4a6b9319a3259d24f77771bc8c00d.pdf |
Constrained Diffusion with Trust Sampling | https://openreview.net/forum?id=dJUb9XRoZI | https://openreview.net/forum?id=dJUb9XRoZI | William Huang,Yifeng Jiang,Tom Van Wouwe,Karen Liu | NIPS 2024,Poster | Diffusion models have demonstrated significant promise in various generative tasks; however, they often struggle to satisfy challenging constraints. Our approach addresses this limitation by rethinking training-free loss-guided diffusion from an optimization perspective. We formulate a series of constrained optimizations throughout the inference process of a diffusion model. In each optimization, we allow the sample to take multiple steps along the gradient of the proxy constraint function until we can no longer trust the proxy, according to the variance at each diffusion level. Additionally, we estimate the state manifold of diffusion model to allow for early termination when the sample starts to wander away from the state manifold at each diffusion step. Trust sampling effectively balances between following the unconditional diffusion model and adhering to the loss guidance, enabling more flexible and accurate constrained generation. We demonstrate the efficacy of our method through extensive experiments on complex tasks, and in drastically different domains of images and 3D motion generation, showing significant improvements over existing methods in terms of generation quality. Our implementation is available at https://github.com/will-s-h/trust-sampling. | https://openreview.net/pdf/e1d858094c12ee2286a240c579e9c76aa92c8a7b.pdf |
Probabilistic Graph Rewiring via Virtual Nodes | https://openreview.net/forum?id=LpvSHL9lcK | https://openreview.net/forum?id=LpvSHL9lcK | Chendi Qian,Andrei Manolache,Christopher Morris,Mathias Niepert | NIPS 2024,Poster | Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning. Despite their effectiveness, MPNNs face challenges such as under-reaching and over-squashing, where limited receptive fields and structural bottlenecks hinder information flow in the graph. While graph transformers hold promise in addressing these issues, their scalability is limited due to quadratic complexity regarding the number of nodes, rendering them impractical for larger graphs. Here, we propose implicitly rewired message-passing neural networks (IPR-MPNNs), a novel approach that integrates implicit probabilistic graph rewiring into MPNNs. By introducing a small number of virtual nodes, i.e., adding additional nodes to a given graph and connecting them to existing nodes, in a differentiable, end-to-end manner, IPR-MPNNs enable long-distance message propagation, circumventing quadratic complexity. Theoretically, we demonstrate that IPR-MPNNs surpass the expressiveness of traditional MPNNs. Empirically, we validate our approach by showcasing its ability to mitigate under-reaching and over-squashing effects, achieving state-of-the-art performance across multiple graph datasets. Notably, IPR-MPNNs outperform graph transformers while maintaining significantly faster computational efficiency. | https://openreview.net/pdf/e3c70652293f00541a7b1d204c28e31a80eabc54.pdf |
Tight Rates for Bandit Control Beyond Quadratics | https://openreview.net/forum?id=mlm3nUwOeQ | https://openreview.net/forum?id=mlm3nUwOeQ | Y. Jennifer Sun,Zhou Lu | NIPS 2024,Poster | Unlike classical control theory, such as Linear Quadratic Control (LQC), real-world control problems are highly complex. These problems often involve adversarial perturbations, bandit feedback models, and non-quadratic, adversarially chosen cost functions. A fundamental yet unresolved question is whether optimal regret can be achieved for these general control problems. The standard approach to addressing this problem involves a reduction to bandit convex optimization with memory. In the bandit setting, constructing a gradient estimator with low variance is challenging due to the memory structure and non-quadratic loss functions.
In this paper, we provide an affirmative answer to this question. Our main contribution is an algorithm that achieves an $\tilde{O}(\sqrt{T})$ optimal regret for bandit non-stochastic control with strongly-convex and smooth cost functions in the presence of adversarial perturbations, improving the previously known $\tilde{O}(T^{2/3})$ regret bound from \citep{cassel2020bandit}. Our algorithm overcomes the memory issue by reducing the problem to Bandit Convex Optimization (BCO) without memory and addresses general strongly-convex costs using recent advancements in BCO from \citep{suggala2024second}. Along the way, we develop an improved algorithm for BCO with memory, which may be of independent interest. | https://openreview.net/pdf/21571625278a93f35acb6aaca70e4b0bb0577e37.pdf |
Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Vision Foundation Models | https://openreview.net/forum?id=loQCk0qruU | https://openreview.net/forum?id=loQCk0qruU | Deep Shankar Pandey,Spandan Pyakurel,Qi Yu | NIPS 2024,Poster | Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance. Parameter efficient fine-tuning ($\texttt{PEFT}$) provides promising generalization performance in adaptation while incurring minimum computational overhead. However, adaptation of these foundation models through $\texttt{PEFT}$ leads to accurate but severely underconfident models, especially in few-shot learning settings. Moreover, the adapted models lack accurate fine-grained uncertainty quantification capabilities limiting their broader applicability in critical domains. To fill out this critical gap, we develop a novel lightweight {Bayesian Parameter Efficient Fine-Tuning} (referred to as $\texttt{Bayesian-PEFT}$) framework for large transformer-based foundation models. The framework integrates state-of-the-art $\texttt{PEFT}$ techniques with two Bayesian components to address the under-confidence issue while ensuring reliable prediction under challenging few-shot settings. The first component performs base rate adjustment to strengthen the prior belief corresponding to the knowledge gained through pre-training, making the model more confident in its predictions; the second component builds an evidential ensemble that leverages belief regularization to ensure diversity among different ensemble components.
Our thorough theoretical analysis justifies that the Bayesian components can ensure reliable and accurate few-shot adaptations with well-calibrated uncertainty quantification. Extensive experiments across diverse datasets, few-shot learning scenarios, and multiple $\texttt{PEFT}$ techniques demonstrate the outstanding prediction and calibration performance by $\texttt{Bayesian-PEFT}$. | https://openreview.net/pdf/0f211c89a99253cf0f4d97fe37005d821b00cfa5.pdf |
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search | https://openreview.net/forum?id=9SpWvX9ykp | https://openreview.net/forum?id=9SpWvX9ykp | Nicola Dainese,Matteo Merler,Minttu Alakuijala,Pekka Marttinen | NIPS 2024,Poster | In this work we consider Code World Models, world models generated by a Large Language Model (LLM) in the form of Python code for model-based Reinforcement Learning (RL). Calling code instead of LLMs for planning has potential to be more precise, reliable, interpretable, and extremely efficient.
However, writing appropriate Code World Models requires the ability to understand complex instructions, to generate exact code with non-trivial logic and to self-debug a long program with feedback from unit tests and environment trajectories. To address these challenges, we propose Generate, Improve and Fix with Monte Carlo Tree Search (GIF-MCTS), a new code generation strategy for LLMs. To test our approach in an offline RL setting, we introduce the Code World Models Benchmark (CWMB), a suite of program synthesis and planning tasks comprised of 18 diverse RL environments paired with corresponding textual descriptions and curated trajectories. GIF-MCTS surpasses all baselines on the CWMB and two other benchmarks, and we show that the Code World Models synthesized with it can be successfully used for planning, resulting in model-based RL agents with greatly improved sample efficiency and inference speed. | https://openreview.net/pdf/86db2e1fa0623f682614587d7c2e40d308e39203.pdf |
Parametric model reduction of mean-field and stochastic systems via higher-order action matching | https://openreview.net/forum?id=qyaz3XP0FN | https://openreview.net/forum?id=qyaz3XP0FN | Jules Berman,Tobias Blickhan,Benjamin Peherstorfer | NIPS 2024,Poster | The aim of this work is to learn models of population dynamics of physical systems that feature stochastic and mean-field effects and that depend on physics parameters. The learned models can act as surrogates of classical numerical models to efficiently predict the system behavior over the physics parameters. Building on the Benamou-Brenier formula from optimal transport and action matching, we use a variational problem to infer parameter- and time-dependent gradient fields that represent approximations of the population dynamics. The inferred gradient fields can then be used to rapidly generate sample trajectories that mimic the dynamics of the physical system on a population level over varying physics parameters. We show that combining Monte Carlo sampling with higher-order quadrature rules is critical for accurately estimating the training objective from sample data and for stabilizing the training process. We demonstrate on Vlasov-Poisson instabilities as well as on high-dimensional particle and chaotic systems that our approach accurately predicts population dynamics over a wide range of parameters and outperforms state-of-the-art diffusion-based and flow-based modeling that simply condition on time and physics parameters. | https://openreview.net/pdf/4f95db67d2e94e6fdc17fe1ba3eacae2b3028669.pdf |
Scalable Optimization in the Modular Norm | https://openreview.net/forum?id=SFxAjB7UXx | https://openreview.net/forum?id=SFxAjB7UXx | Tim Large,Yang Liu,Minyoung Huh,Hyojin Bahng,Phillip Isola,Jeremy Bernstein | NIPS 2024,Poster | To improve performance in contemporary deep learning, one is interested in scaling up the neural network in terms of both the number and the size of the layers. When ramping up the width of a single layer, graceful scaling of training has been linked to the need to normalize the weights and their updates in the "natural norm" particular to that layer. In this paper, we significantly generalize this idea by defining the modular norm, which is the natural norm on the full weight space of any neural network architecture. The modular norm is defined recursively in tandem with the network architecture itself. We show that the modular norm has several promising applications. On the practical side, the modular norm can be used to normalize the updates of any base optimizer so that the learning rate becomes transferable across width and depth. This means that the user does not need to compute optimizer-specific scale factors in order to scale training. On the theoretical side, we show that for any neural network built from "well-behaved" atomic modules, the gradient of the network is Lipschitz-continuous in the modular norm, with the Lipschitz constant admitting a simple recursive formula. This characterization opens the door to porting standard ideas in optimization theory over to deep learning. We have created a Python package called Modula that automatically normalizes weight updates in the modular norm of the architecture. Both the Modula package and code for our experiments are provided in the supplementary material. | https://openreview.net/pdf/f3aa0267adde40f2438c5f134aac596b9e198960.pdf |
Identifying Latent State-Transition Processes for Individualized Reinforcement Learning | https://openreview.net/forum?id=kREpCQtHdN | https://openreview.net/forum?id=kREpCQtHdN | Yuewen Sun,Biwei Huang,Yu Yao,Donghuo Zeng,Xinshuai Dong,Songyao Jin,Boyang Sun,Roberto Legaspi,Kazushi Ikeda,Peter Spirtes,Kun Zhang | NIPS 2024,Poster | In recent years, the application of reinforcement learning (RL) involving interactions with individuals has seen significant growth. These interactions, influenced by individual-specific factors ranging from personal preferences to physiological differences, can causally affect state transitions, such as the health conditions in healthcare or learning progress in education. Consequently, different individuals may exhibit different state-transition processes. Understanding these individualized state-transition processes is crucial for optimizing individualized policies. In practice, however, identifying these state-transition processes is challenging, especially since individual-specific factors often remain latent. In this paper, we establish the identifiability of these latent factors and present a practical method that effectively learns these processes from observed state-action trajectories. Our experiments on various datasets show that our method can effectively identify the latent state-transition processes and help learn individualized RL policies. | https://openreview.net/pdf/85ac46c122945045e96dafd261221d1a23e4ec95.pdf |
Back to the Continuous Attractor | https://openreview.net/forum?id=fvG6ZHrH0B | https://openreview.net/forum?id=fvG6ZHrH0B | Ábel Ságodi,Guillermo Martín-Sánchez,Piotr A Sokol,Il Memming Park | NIPS 2024,Poster | Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals.
Unfortunately, continuous attractors suffer from severe structural instability in general---they are destroyed by most infinitesimal changes of the dynamical law that defines them.
This fragility limits their utility especially in biological systems as their recurrent dynamics are subject to constant perturbations.
We observe that the bifurcations from continuous attractors in theoretical neuroscience models display various structurally stable forms.
Although their asymptotic behaviors to maintain memory are categorically distinct, their finite-time behaviors are similar.
We build on the persistent manifold theory to explain the commonalities between bifurcations from and approximations of continuous attractors.
Fast-slow decomposition analysis uncovers the existence of a persistent slow manifold that survives the seemingly destructive bifurcation, relating the flow within the manifold to the size of the perturbation. Moreover, this allows the bounding of the memory error of these approximations of continuous attractors.
Finally, we train recurrent neural networks on analog memory tasks to support the appearance of these systems as solutions and their generalization capabilities.
Therefore, we conclude that continuous attractors are functionally robust and remain useful as a universal analogy for understanding analog memory. | https://openreview.net/pdf/c5d74247c35d26fced9942711f051a6415b08bbc.pdf |
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization | https://openreview.net/forum?id=JNl6h3U3oW | https://openreview.net/forum?id=JNl6h3U3oW | Haoran You,Yipin Guo,Yichao Fu,Wei Zhou,Huihong Shi,Xiaofan Zhang,Souvik Kundu,Amir Yazdanbakhsh,Yingyan Celine Lin | NIPS 2024,Poster | Large language models (LLMs) have shown impressive performance on language tasks but face challenges when deployed on resource-constrained devices due to their extensive parameters and reliance on dense multiplications, resulting in high memory demands and latency bottlenecks. Shift-and-add reparameterization offers a promising solution by replacing costly multiplications with hardware-friendly primitives in both the attention and multi-layer perceptron (MLP) layers of an LLM. However, current reparameterization techniques require training from scratch or full parameter fine-tuning to restore accuracy, which is resource-intensive for LLMs. To address this, we propose accelerating pretrained LLMs through post-training shift-and-add reparameterization, creating efficient multiplication-free models, dubbed ShiftAddLLM. Specifically, we quantize each weight matrix into binary matrices paired with group-wise scaling factors. The associated multiplications are reparameterized into (1) shifts between activations and scaling factors and (2) queries and adds according to the binary matrices. To reduce accuracy loss, we present a multi-objective optimization method to minimize both weight and output activation reparameterization errors. Additionally, based on varying sensitivity across layers to reparameterization, we develop an automated bit allocation strategy to further reduce memory usage and latency. Experiments on five LLM families and eight tasks consistently validate the effectiveness of ShiftAddLLM, achieving average perplexity reductions of 5.6 and 22.7 points at comparable or lower latency compared to the most competitive quantized LLMs at 3- and 2-bit precision, respectively, and more than 80% memory and energy reductions over the original LLMs. Codes and models are available at https://github.com/GATECH-EIC/ShiftAddLLM. | https://openreview.net/pdf/dbaa21ce19cb724f3f9cb7dcaa53e0ea77e53334.pdf |
Noether's Razor: Learning Conserved Quantities | https://openreview.net/forum?id=dpvqBkEp1f | https://openreview.net/forum?id=dpvqBkEp1f | Tycho F. A. van der Ouderaa,Mark van der Wilk,Pim De Haan | NIPS 2024,Poster | Symmetries have proven useful in machine learning models, improving generalisation and overall performance. At the same time, recent advancements in learning dynamical systems rely on modelling the underlying Hamiltonian to guarantee the conservation of energy.
These approaches can be connected via a seminal result in mathematical physics: Noether's theorem, which states that symmetries in a dynamical system correspond to conserved quantities.
This work uses Noether's theorem to parameterise symmetries as learnable conserved quantities. We then allow conserved quantities and associated symmetries to be learned directly from train data through approximate Bayesian model selection, jointly with the regular training procedure. As training objective, we derive a variational lower bound to the marginal likelihood. The objective automatically embodies an Occam's Razor effect that avoids collapse of conversation laws to the trivial constant, without the need to manually add and tune additional regularisers. We demonstrate a proof-of-principle on n-harmonic oscillators and n-body systems. We find that our method correctly identifies the correct conserved quantities and U(n) and SE(n) symmetry groups, improving overall performance and predictive accuracy on test data. | https://openreview.net/pdf/e7c16ba035c5c4994f07a3aa0f69eb4e452dfbde.pdf |
Credal Learning Theory | https://openreview.net/forum?id=AH5KwUSsln | https://openreview.net/forum?id=AH5KwUSsln | Michele Caprio,Maryam Sultana,Eleni Elia,Fabio Cuzzolin | NIPS 2024,Poster | Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from an unknown probability distribution. In actual deployment, however, the data distribution may (and often does) vary, causing domain adaptation/generalization issues. In this paper we lay the foundations for a `credal' theory of learning, using convex sets of probabilities (credal sets) to model the variability in the data-generating distribution. Such credal sets, we argue, may be inferred from a finite sample of training sets. Bounds are derived for the case of finite hypotheses spaces (both assuming realizability or not), as well as infinite model spaces, which directly generalize classical results. | https://openreview.net/pdf/19306051f44e70ecb2b1164b1ce92657b16c483b.pdf |
Distribution Learning with Valid Outputs Beyond the Worst-Case | https://openreview.net/forum?id=L7i5FjgKjc | https://openreview.net/forum?id=L7i5FjgKjc | Nicholas Rittler,Kamalika Chaudhuri | NIPS 2024,Poster | Generative models at times produce "invalid" outputs, such as images with generation artifacts and unnatural sounds. Validity-constrained distribution learning attempts to address this problem by requiring that the learned distribution have a provably small fraction of its mass in invalid parts of space -- something which standard loss minimization does not always ensure. To this end, a learner in this model can guide the learning via "validity queries", which allow it to ascertain the validity of individual examples. Prior work on this problem takes a worst-case stance, showing that proper learning requires an exponential number of validity queries, and demonstrating an improper algorithm which -- while generating guarantees in a wide-range of settings -- makes a relatively large polynomial number of validity queries. In this work, we take a first step towards characterizing regimes where guaranteeing validity is easier than in the worst-case. We show that when the data distribution lies in the model class and the log-loss is minimized, the number samples required to ensure validity has a weak dependence on the validity requirement. Additionally, we show that when the validity region belongs to a VC-class, a limited number of validity queries are often sufficient. | https://openreview.net/pdf/c2cb0299f9ccbbc0514a603d3440c500c530a129.pdf |
Online Control in Population Dynamics | https://openreview.net/forum?id=ZBBrBujopT | https://openreview.net/forum?id=ZBBrBujopT | Noah Golowich,Elad Hazan,Zhou Lu,Dhruv Rohatgi,Y. Jennifer Sun | NIPS 2024,Poster | The study of population dynamics originated with early sociological works but has since extended into many fields, including biology, epidemiology, evolutionary game theory, and economics. Most studies on population dynamics focus on the problem of prediction rather than control. Existing mathematical models for population control are often restricted to specific, noise-free dynamics, while real-world population changes can be complex and adversarial.
To address this gap, we propose a new framework based on the paradigm of online control. We first characterize a set of linear dynamical systems that can naturally model evolving populations. We then give an efficient gradient-based controller for these systems, with near-optimal regret bounds with respect to a broad class of linear policies. Our empirical evaluations demonstrate the effectiveness of the proposed algorithm for population control even in non-linear models such as SIR and replicator dynamics. | https://openreview.net/pdf/dcccb3b68a8f2187bf2814b594c28b266dd7bc08.pdf |
Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity | https://openreview.net/forum?id=n5lLSskwtu | https://openreview.net/forum?id=n5lLSskwtu | Dayou Yu,Minghao Li,Weishi Shi,Qi Yu | NIPS 2024,Poster | Multi-label active learning is a crucial yet challenging area in contemporary machine learning, often complicated by a large and sparse label space. This challenge is further exacerbated in active learning scenarios where labeling resources are constrained. Drawing inspiration from existing mixture of Bernoulli models, which efficiently compress the label space into a more manageable weight coefficient space by learning correlated Bernoulli components, we propose a novel model called Evidential Mixture Machines (EMM). Our model leverages mixture components derived from unsupervised learning in the label space and improves prediction accuracy by predicting weight coefficients following the evidential learning paradigm. These coefficients are aggregated as proxy pseudo counts to enhance component offset predictions. The evidential learning approach provides an uncertainty-aware connection between input features and the predicted coefficients and components. Additionally, our method combines evidential uncertainty with predicted label embedding covariances for active sample selection, creating a richer, multi-source uncertainty metric beyond traditional uncertainty scores. Experiments on synthetic datasets show the effectiveness of evidential uncertainty prediction and EMM's capability to capture label correlations through predicted components. Further testing on real-world datasets demonstrates improved performance compared to existing multi-label active learning methods. | https://openreview.net/pdf/0ebfc3e5e8ec8b72e72310ea6967dd4bc4e22452.pdf |
On the Scalability of GNNs for Molecular Graphs | https://openreview.net/forum?id=klqhrq7fvB | https://openreview.net/forum?id=klqhrq7fvB | Maciej Sypetkowski,Frederik Wenkel,Farimah Poursafaei,Nia Dickson,Karush Suri,Philip Fradkin,Dominique Beaini | NIPS 2024,Poster | Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures. We address this drawback of GNNs by studying their scaling behavior. Specifically, we analyze message-passing networks, graph Transformers, and hybrid architectures on the largest public collection of 2D molecular graphs for supervised pretraining. For the first time, we observe that GNNs benefit tremendously from the increasing scale of depth, width, number of molecules and associated labels. A major factor is the diversity of the pretraining data that comprises thousands of labels per molecule derived from bio-assays, quantum simulations, transcriptomics and phenomic imaging. We further demonstrate strong finetuning scaling behavior on 38 highly competitive downstream tasks, outclassing previous large models. This gives rise to MolGPS, a new graph foundation model that allows to navigate the chemical space, outperforming the previous state-of-the-arts on 26 out the 38 downstream tasks. We hope that our work paves the way for an era where foundational GNNs drive pharmaceutical drug discovery. | https://openreview.net/pdf/10d89ae98e5eb08d3571feb3470901a034427b55.pdf |
DiffPO: A causal diffusion model for learning distributions of potential outcomes | https://openreview.net/forum?id=merJ77Jipt | https://openreview.net/forum?id=merJ77Jipt | Yuchen Ma,Valentyn Melnychuk,Jonas Schweisthal,Stefan Feuerriegel | NIPS 2024,Poster | Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of causal inference. Existing methods are largely limited to point estimates of potential outcomes with no uncertain quantification; thus, the full information about the distributions of potential outcomes is typically ignored. In this paper, we propose a novel causal diffusion model called DiffPO, which is carefully designed for reliable inferences in medicine by learning the distribution of potential outcomes. In our DiffPO, we leverage a tailored conditional denoising diffusion model to learn complex distributions, where we address the selection bias through a novel orthogonal diffusion loss. Another strength of our DiffPO method is that it is highly flexible (e.g., it can also be used to estimate different causal quantities such as CATE). Across a wide range of experiments, we show that our method achieves state-of-the-art performance. | https://openreview.net/pdf/3ab42b4019b91a4da0db8fd15de4fc431fae84d8.pdf |
Online Learning with Sublinear Best-Action Queries | https://openreview.net/forum?id=9uKeqtIoGZ | https://openreview.net/forum?id=9uKeqtIoGZ | Matteo Russo,Andrea Celli,Riccardo Colini Baldeschi,Federico Fusco,Daniel Haimovich,Dima Karamshuk,Stefano Leonardi,Niek Tax | NIPS 2024,Poster | In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred. Following the recent line of research on algorithms endowed with additional predictive features, we revisit this problem by allowing the decision maker to acquire additional information on the actions to be selected. In particular, we study the power of \emph{best-action queries}, which reveal beforehand the identity of the best action at a given time step. In practice, predictive features may be expensive, so we allow the decision maker to issue at most $k$ such queries.
We establish tight bounds on the performance any algorithm can achieve when given access to $k$ best-action queries for different types of feedback models. In particular, we prove that in the full feedback model, $k$ queries are enough to achieve an optimal regret of $\Theta(\min\{\sqrt T, \frac{T}{k}\})$. This finding highlights the significant multiplicative advantage in the regret rate achievable with even a modest (sublinear) number $k \in \Omega(\sqrt{T})$ of queries.
Additionally, we study the challenging setting in which the only available feedback is obtained during the time steps corresponding to the $k$ best-action queries. There, we provide a tight regret rate of $\Theta(\min\{\frac{T}{\sqrt k},\frac{T^2}{k^2}\})$, which improves over the standard $\Theta(\frac{T}{\sqrt k})$ regret rate for label efficient prediction for $k \in \Omega(T^{2/3})$. | https://openreview.net/pdf/92da4f5f1c9c98618f22314dda0744a9205f3325.pdf |
Parallelizing Linear Transformers with the Delta Rule over Sequence Length | https://openreview.net/forum?id=y8Rm4VNRPH | https://openreview.net/forum?id=y8Rm4VNRPH | Songlin Yang,Bailin Wang,Yu Zhang,Yikang Shen,Yoon Kim | NIPS 2024,Poster | Transformers with linear attention (i.e., linear transformers) and state-space models have recently been suggested as a viable linear-time alternative to transformers with softmax attention. However, these models still underperform transformers especially on tasks that require in-context retrieval. While more expressive variants of linear transformers which replace the additive update in linear transformers with the delta rule (DeltaNet) have been found to be more effective at associative recall, existing algorithms for training such models do not parallelize over sequence length and are thus inefficient to train on modern hardware. This work describes a hardware-efficient algorithm for training linear transformers with the delta rule, which exploits a memory-efficient representation for computing products of Householder matrices. This algorithm allows us to scale up DeltaNet to standard language modeling settings. We train a 1.3B model for 100B tokens and find that it outperforms recent linear-time baselines such as Mamba and GLA in terms of perplexity and zero-shot performance on downstream tasks. We also experiment with two hybrid models which combine DeltaNet layers with (1) sliding-window attention layers every other layer or (2) two global attention layers, and find that these hybrids outperform strong transformer baselines. | https://openreview.net/pdf/66ae05815e599e785fe690f90966433fc3f19b1b.pdf |
Preference Alignment with Flow Matching | https://openreview.net/forum?id=EKN8AGS1wG | https://openreview.net/forum?id=EKN8AGS1wG | Minu Kim,Yongsik Lee,Sehyeok Kang,Jihwan Oh,Song Chong,Se-Young Yun | NIPS 2024,Poster | We present Preference Flow Matching (PFM), a new framework for preference alignment that streamlines the integration of preferences into an arbitrary class of pre-trained models. Existing alignment methods require fine-tuning pre-trained models, which presents challenges such as scalability, inefficiency, and the need for model modifications, especially with black-box APIs like GPT-4. In contrast, PFM utilizes flow matching techniques to directly learn from preference data, thereby reducing the dependency on extensive fine-tuning of pre-trained models. By leveraging flow-based models, PFM transforms less preferred data into preferred outcomes, and effectively aligns model outputs with human preferences without relying on explicit or implicit reward function estimation, thus avoiding common issues like overfitting in reward models. We provide theoretical insights that support our method’s alignment with standard preference alignment objectives. Experimental results indicate the practical effectiveness of our method, offering a new direction in aligning a pre-trained model to preference. Our code is available at https://github.com/jadehaus/preference-flow-matching. | https://openreview.net/pdf/6a7e75898419ae05e0ab639a8fd5fe7e29f2b0e5.pdf |
Dynamic Service Fee Pricing under Strategic Behavior: Actions as Instruments and Phase Transition | https://openreview.net/forum?id=Tnl2K6Iz9j | https://openreview.net/forum?id=Tnl2K6Iz9j | Rui Ai,David Simchi-Levi,Feng Zhu | NIPS 2024,Poster | We study a dynamic pricing problem for third-party platform service fees under strategic, far-sighted customers. In each time period, the platform sets a service fee based on historical data, observes the resulting transaction quantities, and collects revenue. The platform also monitors equilibrium prices influenced by both demand and supply. The objective is to maximize total revenue over a time horizon $T$. Our problem incorporates three practical challenges: (a) initially, the platform lacks knowledge of the demand side beforehand, necessitating a balance between exploring (learning the demand curve) and exploiting (maximizing revenue) simultaneously; (b) since only equilibrium prices and quantities are observable, traditional Ordinary Least Squares (OLS) estimators would be biased and inconsistent; (c) buyers are rational and strategic, seeking to maximize their consumer surplus and potentially misrepresenting their preferences. To address these challenges, we propose novel algorithmic solutions. Our approach involves: (i) a carefully designed active randomness injection to balance exploration and exploitation effectively; (ii) using non-i.i.d. actions as instrumental variables (IV) to consistently estimate demand; (iii) a low-switching cost design that promotes nearly truthful buyer behavior. We show an expected regret bound of $\tilde{\mathcal{O}} (\sqrt{T}\wedge\sigma_S^{-2})$ and demonstrate its optimality, up to logarithmic factors, with respect to both the time horizon $T$ and the randomness in supply $\sigma_S$. Despite its simplicity, our model offers valuable insights into the use of actions as estimation instruments, the benefits of low-switching pricing policies in mitigating strategic buyer behavior, and the role of supply randomness in facilitating exploration which leads to a phase transition of policy performance. | https://openreview.net/pdf/46f7434d34df46fc8b6a666002f862599e33e608.pdf |
Boosted Conformal Prediction Intervals | https://openreview.net/forum?id=Tw032H2onS | https://openreview.net/forum?id=Tw032H2onS | Ran Xie,Rina Foygel Barber,Emmanuel Candes | NIPS 2024,Poster | This paper introduces a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage or reduced interval length. We employ machine learning techniques, notably gradient boosting, to systematically improve upon a predefined conformity score function. This process is guided by carefully constructed loss functions that measure the deviation of prediction intervals from the targeted properties. The procedure operates post-training, relying solely on model predictions and without modifying the trained model (e.g., the deep network). Systematic experiments demonstrate that starting from conventional conformal methods, our boosted procedure achieves substantial improvements in reducing interval length and decreasing deviation from target conditional coverage. | https://openreview.net/pdf/b473ef6a51b3c486561543de0461b928c3f4583e.pdf |
The tree autoencoder model, with application to hierarchical data visualization | https://openreview.net/forum?id=Yy0KUmneV6 | https://openreview.net/forum?id=Yy0KUmneV6 | Miguel Á. Carreira-Perpiñán,Kuat Gazizov | NIPS 2024,Poster | We propose a new model for dimensionality reduction, the PCA tree, which works like a regular autoencoder, having explicit projection and reconstruction mappings. The projection is effected by a sparse oblique tree, having hard, hyperplane splits using few features and linear leaves. The reconstruction mapping is a set of local linear mappings. Thus, rather than producing a global map as in t-SNE and other methods, which often leads to distortions, it produces a hierarchical set of local PCAs. The use of a sparse oblique tree and PCA makes the overall model interpretable and very fast to project or reconstruct new points. Joint optimization of all the parameters in the tree is a nonconvex nondifferentiable problem. We propose an algorithm that is guaranteed to decrease the error monotonically and which scales to large datasets without any approximation. In experiments, we show PCA trees are able to identify a wealth of low-dimensional and cluster structure in image and document datasets. | https://openreview.net/pdf/e2fb6778e9b106355a1a94237bf6ac47ee019883.pdf |
Exploration by Learning Diverse Skills through Successor State Representations | https://openreview.net/forum?id=oyiBLfNJvY | https://openreview.net/forum?id=oyiBLfNJvY | Paul-Antoine LE TOLGUENEC,Yann BESSE,Florent Teichteil-Königsbuch,Dennis George Wilson,Emmanuel Rachelson | NIPS 2024,Poster | The ability to perform different skills can encourage agents to explore. In this work, we aim to construct a set of diverse skills that uniformly cover the state space. We propose a formalization of this search for diverse skills, building on a previous definition based on the mutual information between states and skills. We consider the distribution of states reached by a policy conditioned on each skill and leverage the successor state representation to maximize the difference between these skill distributions. We call this approach LEADS: Learning Diverse Skills through Successor State Representations. We demonstrate our approach on a set of maze navigation and robotic control tasks which show that our method is capable of constructing a diverse set of skills which exhaustively cover the state space without relying on reward or exploration bonuses. Our findings demonstrate that this new formalization promotes more robust and efficient exploration by combining mutual information maximization and exploration bonuses. | https://openreview.net/pdf/dd7daaa95d0d7d28d2b3debd6bc2adb0031ae0f9.pdf |
SpecExec: Massively Parallel Speculative Decoding For Interactive LLM Inference on Consumer Devices | https://openreview.net/forum?id=JAhNsZ9dvG | https://openreview.net/forum?id=JAhNsZ9dvG | Ruslan Svirschevski,Avner May,Zhuoming Chen,Beidi Chen,Zhihao Jia,Max Ryabinin | NIPS 2024,Poster | As large language models gain widespread adoption, running them efficiently becomes a crucial task. Recent works on LLM inference use speculative decoding to achieve extreme speedups. However, most of these works implicitly design their algorithms for high-end datacenter hardware. In this work, we ask the opposite question: how fast can we run LLMs on consumer machines? Consumer GPUs can no longer fit the largest available models and must offload them to RAM or SSD. With parameter offloading, hundreds or thousands of tokens can be processed in batches within the same time as just one token, making it a natural fit for speculative decoding. We propose SpecExec (Speculative Execution), a simple parallel decoding method that can generate up to 20 tokens per target model iteration for popular LLM families. SpecExec takes the most probable continuations from the draft model to build a "cache" tree for the target model, which then gets validated in a single pass. Using SpecExec, we demonstrate inference of 50B+ parameter LLMs on consumer GPUs with RAM offloading at 4--6 tokens per second with 4-bit quantization or 2--3 tokens per second with 16-bit weights. Our code is available at https://github.com/yandex-research/specexec . | https://openreview.net/pdf/92424fd5845a7ee52f4c92a728675750ad954baf.pdf |
Interpretable Generalized Additive Models for Datasets with Missing Values | https://openreview.net/forum?id=soUXmwL5aK | https://openreview.net/forum?id=soUXmwL5aK | Hayden McTavish,Jon Donnelly,Margo Seltzer,Cynthia Rudin | NIPS 2024,Poster | Many important datasets contain samples that are missing one or more feature values. Maintaining the interpretability of machine learning models in the presence of such missing data is challenging. Singly or multiply imputing missing values complicates the model’s mapping from features to labels. On the other hand, reasoning on indicator variables that represent missingness introduces a potentially large number of additional terms, sacrificing sparsity. We solve these problems with M-GAM, a sparse, generalized, additive modeling approach that incorporates missingness indicators and their interaction terms while maintaining sparsity through $\ell_0$ regularization. We show that M-GAM provides similar or superior accuracy to prior methods while significantly improving sparsity relative to either imputation or naïve inclusion of indicator variables. | https://openreview.net/pdf/d4ffc16a35baa261ecf09bc0c68828f805c6ec99.pdf |
The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks | https://openreview.net/forum?id=wsHMb4J2o9 | https://openreview.net/forum?id=wsHMb4J2o9 | Lénaïc Chizat,Praneeth Netrapalli | NIPS 2024,Poster | Deep learning succeeds by doing hierarchical feature learning, yet tuning hyper-parameters (HP) such as initialization scales, learning rates etc., only give indirect control over this behavior. In this paper, we introduce a key notion to predict and control feature learning: the angle $\theta_\ell$ between the feature updates and the backward pass (at layer index $\ell$). We show that the magnitude of feature updates after one GD step, at any training time, can be expressed via a simple and general *feature speed formula* in terms of this angle $\theta_\ell$, the loss decay, and the magnitude of the backward pass. This angle $\theta_\ell$ is controlled by the conditioning of the layer-to-layer Jacobians and at random initialization, it is determined by the spectrum of a certain kernel, which coincides with the Neural Tangent Kernel when $\ell=\text{depth}$. Given $\theta_\ell$, the feature speed formula provides us with rules to adjust HPs (scales and learning rates) so as to satisfy certain dynamical properties, such as feature learning and loss decay. We investigate the implications of our approach for ReLU MLPs and ResNets in the large width-then-depth limit. Relying on prior work, we show that in ReLU MLPs with iid initialization, the angle degenerates with depth as $\cos(\theta_\ell)=\Theta(1/\sqrt{\ell})$. In contrast, ResNets with branch scale $O(1/\sqrt{\text{depth}})$ maintain a non-degenerate angle $\cos(\theta_\ell)=\Theta(1)$. We use these insights to recover key properties of known HP scalings (such as $\mu$P), and also introduce a new HP scaling for large depth ReLU MLPs with favorable theoretical properties. | https://openreview.net/pdf/b9078a3029a8ba5f5d9f97f0236d84340733175b.pdf |
Inference of Neural Dynamics Using Switching Recurrent Neural Networks | https://openreview.net/forum?id=zb8jLAh2VN | https://openreview.net/forum?id=zb8jLAh2VN | Yongxu Zhang,Shreya Saxena | NIPS 2024,Poster | Neural population activity often exhibits distinct dynamical features across time, which may correspond to distinct internal processes or behavior. Linear methods and variations thereof, such as Hidden Markov Model (HMM) and Switching Linear Dynamical System (SLDS), are often employed to identify discrete states with evolving neural dynamics. However, these techniques may not be able to capture the underlying nonlinear dynamics associated with neural propagation. Recurrent Neural Networks (RNNs) are commonly used to model neural dynamics thanks to their nonlinear characteristics. In our work, we develop Switching Recurrent Neural Networks (SRNN), RNNs with weights that switch across time, to reconstruct switching dynamics of neural time-series data. We apply these models to simulated data as well as cortical neural activity across mice and monkeys, which allows us to automatically detect discrete states that lead to the identification of varying neural dynamics. In a monkey reaching dataset with electrophysiology recordings, a mouse self-initiated lever pull dataset with widefield calcium recordings, and a mouse self-initiated decision making dataset with widefield calcium recording, SRNNs are able to automatically identify discrete states with distinct nonlinear neural dynamics. The inferred switches are aligned with the behavior, and the reconstructions show that the recovered neural dynamics are distinct across different stages of the behavior. We show that the neural dynamics have behaviorally-relevant switches across time and we are able to use SRNNs to successfully capture these switches and the corresponding dynamical features. | https://openreview.net/pdf/a72b19b658dbec5e7192f749e9871e5279caf5ab.pdf |
Mutli-Armed Bandits with Network Interference | https://openreview.net/forum?id=ZxZOvVOiiL | https://openreview.net/forum?id=ZxZOvVOiiL | Abhineet Agarwal,Anish Agarwal,Lorenzo Masoero,Justin Whitehouse | NIPS 2024,Poster | Online experimentation with interference is a common challenge in modern applications such as e-commerce and adaptive clinical trials in medicine. For example, in online marketplaces, the revenue of a good depends on discounts applied to competing goods. Statistical inference with interference is widely studied in the offline setting, but far less is known about how to adaptively assign treatments to minimize regret. We address this gap by studying a multi-armed bandit (MAB) problem where a learner (e-commerce platform) sequentially assigns one of possible $\mathcal{A}$ actions (discounts) to $N$ units (goods) over $T$ rounds to minimize regret (maximize revenue). Unlike traditional MAB problems, the reward of each unit depends on the treatments assigned to other units, i.e., there is *interference* across the underlying network of units. With $\mathcal{A}$ actions and $N$ units, minimizing regret is combinatorially difficult since the action space grows as $\mathcal{A}^N$. To overcome this issue, we study a *sparse network interference* model, where the reward of a unit is only affected by the treatments assigned to $s$ neighboring units. We use tools from discrete Fourier analysis to develop a sparse linear representation of the unit-specific reward $r_n: [\mathcal{A}]^N \rightarrow \mathbb{R} $, and propose simple, linear regression-based algorithms to minimize regret. Importantly, our algorithms achieve provably low regret both when the learner observes the interference neighborhood for all units and when it is unknown. This significantly generalizes other works on this topic which impose strict conditions on the strength of interference on a *known* network, and also compare regret to a markedly weaker optimal action.
Empirically, we corroborate our theoretical findings via numerical simulations. | https://openreview.net/pdf/7a7932fd0368f67a09848e14871c164691067a1e.pdf |
CIFD: Controlled Information Flow to Enhance Knowledge Distillation | https://openreview.net/forum?id=xutrKezbPF | https://openreview.net/forum?id=xutrKezbPF | Yashas Malur Saidutta,Rakshith Sharma Srinivasa,Jaejin Cho,Ching-Hua Lee,Chouchang Yang,Yilin Shen,Hongxia Jin | NIPS 2024,Poster | Knowledge Distillation is the mechanism by which the insights gained from a larger teacher model are transferred to a smaller student model. However, the transfer suffers when the teacher model is significantly larger than the student. To overcome this, prior works have proposed training intermediately sized models, Teacher Assistants (TAs) to help the transfer process. However, training TAs is expensive, as training these models is a knowledge transfer task in itself. Further, these TAs are larger than the student model and training them especially in large data settings can be computationally intensive. In this paper, we propose a novel framework called Controlled Information Flow for Knowledge Distillation (CIFD) consisting of two components. First, we propose a significantly smaller alternatives to TAs, the Rate-Distortion Module (RDM) which uses the teacher's penultimate layer embedding and a information rate-constrained bottleneck layer to replace the Teacher Assistant model. RDMs are smaller and easier to train than TAs, especially in large data regimes, since they operate on the teacher embeddings and do not need to relearn low level input feature extractors. Also, by varying the information rate across the bottleneck, RDMs can replace TAs of different sizes. Secondly, we propose the use of Information Bottleneck Module in the student model, which is crucial for regularization in the presence of a large number of RDMs. We show comprehensive state-of-the-art results of the proposed method over large datasets like Imagenet. Further, we show the significant improvement in distilling CLIP like models over a huge 12M image-text dataset. It outperforms CLIP specialized distillation methods across five zero-shot classification datasets and two zero-shot image-text retrieval datasets. | https://openreview.net/pdf/bf757aa0647765798d48b492071c603b7edc57aa.pdf |
Communication-Efficient Federated Group Distributionally Robust Optimization | https://openreview.net/forum?id=xNZEjFe0mh | https://openreview.net/forum?id=xNZEjFe0mh | Zhishuai Guo,Tianbao Yang | NIPS 2024,Poster | Federated learning faces challenges due to the heterogeneity in data volumes and distributions at different clients, which can compromise model generalization ability to various distributions.
Existing approaches to address this issue based on group distributionally robust optimization (GDRO) often lead to high communication and sample complexity.
To this end, this work introduces algorithms tailored for communication-efficient Federated Group Distributionally Robust Optimization (FGDRO). Our contributions are threefold: Firstly, we introduce the FGDRO-CVaR algorithm, which optimizes the average top-K losses while reducing communication complexity to $O(1/\epsilon^4)$, where $\epsilon$ denotes the desired precision level. Secondly, our FGDRO-KL algorithm is crafted to optimize KL regularized FGDRO, cutting communication complexity to $O(1/\epsilon^3)$. Lastly, we propose FGDRO-KL-Adam to utilize Adam-type local updates in FGDRO-KL, which not only maintains a communication cost of $O(1/\epsilon^3)$ but also shows potential to surpass SGD-type local steps in practical applications.
The effectiveness of our algorithms has been demonstrated on a variety of real-world tasks, including natural language processing and computer vision. | https://openreview.net/pdf/a65e23614800d52b04091555fc2509133c2dc354.pdf |
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models | https://openreview.net/forum?id=JVKABhr6mP | https://openreview.net/forum?id=JVKABhr6mP | Byung-Kwan Lee,Chae Won Kim,Beomchan Park,Yong Man Ro | NIPS 2024,Poster | The rapid development of large language and vision models (LLVMs) has been driven by advances in visual instruction tuning. Recently, open-source LLVMs have curated high-quality visual instruction tuning datasets and utilized additional vision encoders or multiple computer vision models in order to narrow the performance gap with powerful closed-source LLVMs. These advancements are attributed to multifaceted information required for diverse capabilities, including fundamental image understanding, real-world knowledge about common-sense and non-object concepts (e.g., charts, diagrams, symbols, signs, and math problems), and step-by-step procedures for solving complex questions. Drawing from the multifaceted information, we present a new efficient LLVM, Mamba-based traversal of rationales (Meteor), which leverages multifaceted rationale to enhance understanding and answering capabilities. To embed lengthy rationales containing abundant information, we employ the Mamba architecture, capable of processing sequential data with linear time complexity. We introduce a new concept of traversal of rationale that facilitates efficient embedding of rationale. Subsequently, the backbone multimodal language model (MLM) is trained to generate answers with the aid of rationale. Through these steps, Meteor achieves significant improvements in vision language performances across multiple evaluation benchmarks requiring diverse capabilities, without scaling up the model size or employing additional vision encoders and computer vision models. | https://openreview.net/pdf/d68d63de15506ec221657ddca9ceb58cdd2987ea.pdf |
Preference Learning Algorithms Do Not Learn Preference Rankings | https://openreview.net/forum?id=YkJ5BuEXdD | https://openreview.net/forum?id=YkJ5BuEXdD | Angelica Chen,Sadhika Malladi,Lily H Zhang,Xinyi Chen,Qiuyi Zhang,Rajesh Ranganath,Kyunghyun Cho | NIPS 2024,Poster | Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited. In this work, we study the conventional wisdom that preference learning trains models to assign higher likelihoods to more preferred outputs than less preferred outputs, measured via *ranking accuracy*.
Surprisingly, we find that most state-of-the-art preference-tuned models achieve a ranking accuracy of less than 60% on common preference datasets. We furthermore derive the *idealized ranking accuracy* that a preference-tuned LLM would achieve if it optimized the DPO or RLHF objective perfectly. We demonstrate that existing models exhibit a significant *alignment gap* -- *i.e.*, a gap between the observed and idealized ranking accuracies.
We attribute this discrepancy to the DPO objective, which is empirically and theoretically ill-suited to correct even mild ranking errors in the reference model, and derive a simple and efficient formula for quantifying the difficulty of learning a given preference datapoint.
Finally, we demonstrate that ranking accuracy strongly correlates with the empirically popular win rate metric when the model is close to the reference model used in the objective, shedding further light on the differences between on-policy (e.g., RLHF) and off-policy (e.g., DPO) preference learning algorithms. | https://openreview.net/pdf/95c0afb58459cccd1a6085c7fc9b42a20a055bec.pdf |
Symmetric Linear Bandits with Hidden Symmetry | https://openreview.net/forum?id=aLzA7MSc6Y | https://openreview.net/forum?id=aLzA7MSc6Y | Nam Phuong Tran,The-Anh Ta,Debmalya Mandal,Long Tran-Thanh | NIPS 2024,Poster | High-dimensional linear bandits with low-dimensional structure have received considerable attention in recent studies due to their practical significance. The most common structure in the literature is sparsity. However, it may not be available in practice. Symmetry, where the reward is invariant under certain groups of transformations on the set of arms, is another important inductive bias in the high-dimensional case that covers many standard structures, including sparsity. In this work, we study high-dimensional symmetric linear bandits where the symmetry is hidden from the learner, and the correct symmetry needs to be learned in an online setting. We examine the structure of a collection of hidden symmetry and provide a method based on model selection within the collection of low-dimensional subspaces. Our algorithm achieves a regret bound of $ O(d_0^{2/3} T^{2/3} \log(d))$, where $d$ is the ambient dimension which is potentially very large, and $d_0$ is the dimension of the true low-dimensional subspace such that $d_0 \ll d$. With an extra assumption on well-separated models, we can further improve the regret to $ O(d_0 \sqrt{T\log(d)} )$. | https://openreview.net/pdf/1336a7d4d4672120c9554c0969fd58460f1fd738.pdf |
SAMPa: Sharpness-aware Minimization Parallelized | https://openreview.net/forum?id=IGn0ktYDwV | https://openreview.net/forum?id=IGn0ktYDwV | Wanyun Xie,Thomas Pethick,Volkan Cevher | NIPS 2024,Poster | Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, each SAM update requires _sequentially_ computing two gradients, effectively doubling the per-iteration cost compared to base optimizers like SGD. We propose a simple modification of SAM, termed SAMPa, which allows us to fully parallelize the two gradient computations. SAMPa achieves a twofold speedup of SAM under the assumption that communication costs between devices are negligible. Empirical results show that SAMPa ranks among the most efficient variants of SAM in terms of computational time. Additionally, our method consistently outperforms SAM across both vision and language tasks. Notably, SAMPa theoretically maintains convergence guarantees even for _fixed_ perturbation sizes, which is established through a novel Lyapunov function. We in fact arrive at SAMPa by treating this convergence guarantee as a hard requirement---an approach we believe is promising for developing SAM-based methods in general. Our code is available at https://github.com/LIONS-EPFL/SAMPa. | https://openreview.net/pdf/528cbf9a7eae9b617350469e36dedd94081c01bc.pdf |
Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization | https://openreview.net/forum?id=u2gzfXRLaN | https://openreview.net/forum?id=u2gzfXRLaN | Omar Montasser,Han Shao,Emmanuel Abbe | NIPS 2024,Poster | Learning with identical train and test distributions has been extensively investigated both practically and theoretically. Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps. We initiate a theoretical study for this framework, investigating learning scenarios where the target class of transformations is either known or unknown. We establish learning rules and algorithmic reductions to Empirical Risk Minimization (ERM), accompanied with learning guarantees. We obtain upper bounds on the sample complexity in terms of the VC dimension of the class composing predictors with transformations, which we show in many cases is not much larger than the VC dimension of the class of predictors. We highlight that the learning rules we derive offer a game-theoretic viewpoint on distribution shift: a learner searching for predictors and an adversary searching for transformation maps to respectively minimize and maximize the worst-case loss. | https://openreview.net/pdf/6df5ab210a13b0c96e72de1101a3d2ac1755a6fd.pdf |
A Theory of Optimistically Universal Online Learnability for General Concept Classes | https://openreview.net/forum?id=EAbNopo3os | https://openreview.net/forum?id=EAbNopo3os | Steve Hanneke,Hongao Wang | NIPS 2024,Poster | We provide a full characterization of the concept classes that are optimistically universally online learnable with {0, 1} labels. The notion of optimistically universal online learning was defined in [Hanneke, 2021] in order to understand learnability under minimal assumptions. In this paper, following the philosophy behind that work, we investigate two questions, namely, for every concept class: (1) What are the minimal assumptions on the data process admitting online learnability? (2) Is there a learning algorithm which succeeds under every data process satisfying the minimal assumptions? Such an algorithm is said to be optimistically universal for the given concept class. We resolve both of these questions for all concept classes, and moreover, as part of our solution we design general learning algorithms for each case. Finally, we extend these algorithms and results to the agnostic case, showing an equivalence between the minimal assumptions on the data process for learnability in the agnostic and realizable cases, for every concept class, as well as the equivalence of optimistically universal learnability. | https://openreview.net/pdf/27e398868d5b35ab4ec0f66658b41b4f93dfcfec.pdf |
The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons | https://openreview.net/forum?id=VNmi0FHn6Z | https://openreview.net/forum?id=VNmi0FHn6Z | Eryn Sale,Wenhao Zhang | NIPS 2024,Poster | Accumulating evidence suggests stochastic cortical circuits can perform sampling-based Bayesian inference to compute the latent stimulus posterior. Canonical cortical circuits consist of excitatory (E) neurons and types of inhibitory (I) interneurons. Nevertheless, nearly no sampling neural circuit models consider the diversity of interneurons, and thus how interneurons contribute to sampling remains poorly understood. To provide theoretical insight, we build a nonlinear canonical circuit model consisting of recurrently connected E neurons and two types of I neurons including Parvalbumin (PV) and Somatostatin (SOM) neurons. The E neurons are modeled as a canonical ring (attractor) model, receiving global inhibition from PV neurons, and locally tuning-dependent inhibition from SOM neurons.
We theoretically analyze the nonlinear circuit dynamics and analytically identify the Bayesian sampling algorithm performed by the circuit dynamics. We found a reduced circuit with only E and PV neurons performs Langevin sampling, and the inclusion of SOM neurons with tuning-dependent inhibition speeds up the sampling via upgrading the Langevin into Hamiltonian sampling. Moreover, the Hamiltonian framework requires SOM neurons to receive no direct feedforward connections, consistent with neuroanatomy. Our work provides overarching connections between nonlinear circuits with various types of interneurons and sampling algorithms, deepening our understanding of circuit implementation of Bayesian inference. | https://openreview.net/pdf/b1876afc8494a337b561fc0b83ea09026756814e.pdf |
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation | https://openreview.net/forum?id=1iHmhMHNyA | https://openreview.net/forum?id=1iHmhMHNyA | Jiawei Wang,Renhe Jiang,Chuang Yang,Zengqing Wu,Makoto Onizuka,Ryosuke Shibasaki,Noboru Koshizuka,Chuan Xiao | NIPS 2024,Poster | This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis. | https://openreview.net/pdf/68fd30200398640d70b139a21fd58f711886738c.pdf |
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data | https://openreview.net/forum?id=uO53206oLJ | https://openreview.net/forum?id=uO53206oLJ | Jiaojiao Zhang,Jiang Hu,Anthony Man-Cho So,Mikael Johansson | NIPS 2024,Poster | Many machine learning tasks, such as principal component analysis and low-rank matrix completion, give rise to manifold optimization problems. Although there is a large body of work studying the design and analysis of algorithms for manifold optimization in the centralized setting, there are currently very few works addressing the federated setting. In this paper, we consider nonconvex federated learning
over a compact smooth submanifold in the setting of heterogeneous client data. We propose an algorithm that leverages stochastic Riemannian gradients and a manifold projection operator to improve computational efficiency, uses local updates to improve communication efficiency, and avoids client drift. Theoretically, we show that our proposed algorithm converges sub-linearly to a neighborhood of a first-order optimal solution by using a novel analysis that jointly exploits the manifold structure and properties of the loss functions. Numerical experiments demonstrate that our algorithm has significantly smaller computational and communication overhead than existing methods. | https://openreview.net/pdf/0a6ebdd721a6f62f7e75a245c3c48a52794ba892.pdf |
Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis | https://openreview.net/forum?id=AFnSMlye5K | https://openreview.net/forum?id=AFnSMlye5K | Jiayu Su,David A. Knowles,Raul Rabadan | NIPS 2024,Poster | The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more than one space, while more expressive deep learning approaches lack interpretability. Here, we introduce Supervised Independent Subspace Principal Component Analysis ($\texttt{sisPCA}$), a PCA extension designed for multi-subspace learning. Leveraging the Hilbert-Schmidt Independence Criterion (HSIC), $\texttt{sisPCA}$ incorporates supervision and simultaneously ensures subspace disentanglement. We demonstrate $\texttt{sisPCA}$'s connections with autoencoders and regularized linear regression and showcase its ability to identify and separate hidden data structures through extensive applications, including breast cancer diagnosis from image features, learning aging-associated DNA methylation changes, and single-cell analysis of malaria infection. Our results reveal distinct functional pathways associated with malaria colonization, underscoring the essentiality of explainable representation in high-dimensional data analysis. | https://openreview.net/pdf/720441a6bfcaf097936eec73adbaf8ecdc6d8b1b.pdf |
Optimal Multiclass U-Calibration Error and Beyond | https://openreview.net/forum?id=7aFRgCC8Q7 | https://openreview.net/forum?id=7aFRgCC8Q7 | Haipeng Luo,Spandan Senapati,Vatsal Sharan | NIPS 2024,Poster | We consider the problem of online multiclass U-calibration, where a forecaster aims to make sequential distributional predictions over $K$ classes with low U-calibration error, that is, low regret with respect to all bounded proper losses simultaneously.
Kleinberg et al. (2023) developed an algorithm with U-calibration error $\mathcal{O}(K\sqrt{T})$ after $T$ rounds and raised the open question of what the optimal bound is.
We resolve this question by showing that the optimal U-calibration error is $\Theta(\sqrt{KT})$ --- we start with a simple observation that the Follow-the-Perturbed-Leader algorithm of Daskalakis and Syrgkanis (2016) achieves this upper bound, followed by a matching lower bound constructed with a specific proper loss (which, as a side result, also proves the optimality of the algorithm of Daskalakis and Syrgkanis (2016) in the context of online learning against an adversary with finite choices).
We also strengthen our results under natural assumptions on the loss functions, including $\Theta(\log T)$ U-calibration error for Lipschitz proper losses, $\mathcal{O}(\log T)$ U-calibration error for a certain class of decomposable proper losses, U-calibration error bounds for proper losses with a low covering number, and others. | https://openreview.net/pdf/82cd1a97753a9cf42f5ae24fcbc2898155113218.pdf |
REBEL: Reinforcement Learning via Regressing Relative Rewards | https://openreview.net/forum?id=yxjWAJzUyV | https://openreview.net/forum?id=yxjWAJzUyV | Zhaolin Gao,Jonathan Daniel Chang,Wenhao Zhan,Owen Oertell,Gokul Swamy,Kianté Brantley,Thorsten Joachims,J. Andrew Bagnell,Jason D. Lee,Wen Sun | NIPS 2024,Poster | While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g. value networks, clipping), and is notorious for its sensitivity to the precise implementation of these components. In response, we take a step back and ask what a *minimalist* RL algorithm for the era of generative models would look like. We propose REBEL, an algorithm that cleanly reduces the problem of policy optimization to regressing the *relative reward* between two completions to a prompt in terms of the policy, enabling strikingly lightweight implementation. In theory, we prove that fundamental RL algorithms like Natural Policy Gradient can be seen as variants of REBEL, which allows us to match the strongest known theoretical guarantees in terms of convergence and sample complexity in the RL literature. REBEL can also cleanly incorporate offline data and be extended to handle the intransitive preferences we frequently see in practice. Empirically, we find that REBEL provides a unified approach to language modeling and image generation with stronger or similar performance as PPO and DPO, all while being simpler to implement and more computationally efficient than PPO. When fine-tuning Llama-3-8B-Instruct, REBEL achieves strong performance in AlpacaEval 2.0, MT-Bench, and Open LLM Leaderboard. Implementation of REBEL can be found at <https://github.com/ZhaolinGao/REBEL>, and models trained by REBEL can be found at <https://huggingface.co/Cornell-AGI>. | https://openreview.net/pdf/593bf12fbcf5e521841f522017b13aceee20e6e5.pdf |
Amortized Active Causal Induction with Deep Reinforcement Learning | https://openreview.net/forum?id=7AXY27kdNH | https://openreview.net/forum?id=7AXY27kdNH | Yashas Annadani,Panagiotis Tigas,Stefan Bauer,Adam Foster | NIPS 2024,Poster | We present Causal Amortized Active Structure Learning (CAASL), an active intervention design policy that can select interventions that are adaptive, real-time and that does not require access to the likelihood. This policy, an amortized network based on the transformer, is trained with reinforcement learning on a simulator of the design environment, and a reward function that measures how close the true causal graph is to a causal graph posterior inferred from the gathered data. On synthetic data and a single-cell gene expression simulator, we demonstrate empirically that the data acquired through our policy results in a better estimate of the underlying causal graph than alternative strategies. Our design policy successfully achieves amortized intervention design on the distribution of the training environment while also generalizing well to distribution shifts in test-time design environments. Further, our policy also demonstrates excellent zero-shot generalization to design environments with dimensionality higher than that during training, and to intervention types that it has not been trained on. | https://openreview.net/pdf/bbc0abf6bfbdb21646362fb7cd41326593efb1b9.pdf |
User-item fairness tradeoffs in recommendations | https://openreview.net/forum?id=ZOZjMs3JTs | https://openreview.net/forum?id=ZOZjMs3JTs | Sophie Greenwood,Sudalakshmee Chiniah,Nikhil Garg | NIPS 2024,Poster | In the basic recommendation paradigm, the most (predicted) relevant item is recommended to each user. This may result in some items receiving lower exposure than they "should"; to counter this, several algorithmic approaches have been developed to ensure *item fairness*. These approaches necessarily degrade recommendations for some users to improve outcomes for items, leading to *user fairness* concerns. In turn, a recent line of work has focused on developing algorithms for multi-sided fairness, to jointly optimize user fairness, item fairness, and overall recommendation quality. This induces the question: *what is the tradeoff between these objectives, and what are the characteristics of (multi-objective) optimal solutions?* Theoretically, we develop a model of recommendations with user and item fairness objectives and characterize the solutions of fairness-constrained optimization. We identify two phenomena: (a) when user preferences are diverse, there is "free" item and user fairness; and (b) users whose preferences are misestimated can be *especially* disadvantaged by item fairness constraints. Empirically, we prototype a recommendation system for preprints on arXiv and implement our framework, measuring the phenomena in practice and showing how these phenomena inform the *design* of markets with recommendation systems-intermediated matching. | https://openreview.net/pdf/a6194046ea76f3fed36b606423dbac2c3a30f65d.pdf |
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication | https://openreview.net/forum?id=DUHX779C5q | https://openreview.net/forum?id=DUHX779C5q | Huao Li,Hossein Nourkhiz Mahjoub,Behdad Chalaki,Vaishnav Tadiparthi,Kwonjoon Lee,Ehsan Moradi Pari,Charles Michael Lewis,Katia P. Sycara | NIPS 2024,Poster | Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans or other agents not co-trained together, limiting its applicability in ad-hoc teamwork scenarios. In this work, we propose a novel computational pipeline that aligns the communication space between MARL agents with an embedding space of human natural language by grounding agent communications on synthetic data generated by embodied Large Language Models (LLMs) in interactive teamwork scenarios. Our results demonstrate that introducing language grounding not only maintains task performance but also accelerates the emergence of communication. Furthermore, the learned communication protocols exhibit zero-shot generalization capabilities in ad-hoc teamwork scenarios with unseen teammates and novel task states. This work presents a significant step toward enabling effective communication and collaboration between artificial agents and humans in real-world teamwork settings. | https://openreview.net/pdf/6664b584d62b376a0ad3f0a353ad58f8ef896b2e.pdf |
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning | https://openreview.net/forum?id=HXdAfK488A | https://openreview.net/forum?id=HXdAfK488A | Wasu Top Piriyakulkij,Cassidy Langenfeld,Tuan Anh Le,Kevin Ellis | NIPS 2024,Poster | We give a model of how to infer natural language rules by doing experiments. The
model integrates Large Language Models (LLMs) with Monte Carlo algorithms for
probabilistic inference, interleaving online belief updates with experiment design
under information-theoretic criteria. We conduct a human-model comparison on a
Zendo-style task, finding that a critical ingredient for modeling the human data is to
assume that humans also consider fuzzy, probabilistic rules, in addition to assuming
that humans perform approximately-Bayesian belief updates. We also compare
with recent algorithms for using LLMs to generate and revise hypotheses, finding
that our online inference method yields higher accuracy at recovering the true
underlying rule, and provides better support for designing optimal experiments. | https://openreview.net/pdf/939890c333016bb05e716f12f54bebba3fc3b5dc.pdf |
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically | https://openreview.net/forum?id=SoM3vngOH5 | https://openreview.net/forum?id=SoM3vngOH5 | Anay Mehrotra,Manolis Zampetakis,Paul Kassianik,Blaine Nelson,Hyrum S Anderson,Yaron Singer,Amin Karbasi | NIPS 2024,Poster | While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designed *jailbreaks*. In this work, we present *Tree of Attacks with Pruning* (TAP), an automated method for generating jailbreaks that only requires black-box access to the target LLM. TAP utilizes an attacker LLM to iteratively refine candidate (attack) prompts until one of the refined prompts jailbreaks the target. In addition, before sending prompts to the target, TAP assesses them and prunes the ones unlikely to result in jailbreaks, reducing the number of queries sent to the target LLM. In empirical evaluations, we observe that TAP generates prompts that jailbreak state-of-the-art LLMs (including GPT4-Turbo and GPT4o) for more than 80% of the prompts. This significantly improves upon the previous state-of-the-art black-box methods for generating jailbreaks while using a smaller number of queries than them. Furthermore, TAP is also capable of jailbreaking LLMs protected by state-of-the-art *guardrails*, e.g., LlamaGuard. | https://openreview.net/pdf/4795c11baf761e1c1bfdee844318f70047907116.pdf |
Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood | https://openreview.net/forum?id=uRnTYPkF3V | https://openreview.net/forum?id=uRnTYPkF3V | Ziyi Liu,Idan Attias,Daniel M. Roy | NIPS 2024,Poster | We study the fundamental problem of sequential probability assignment, also known as online learning with logarithmic loss, with respect to an arbitrary, possibly nonparametric hypothesis class. Our goal is to obtain a complexity measure for the hypothesis class that characterizes the minimax regret and to determine a general, minimax optimal algorithm. Notably, the sequential $\ell_{\infty}$ entropy, extensively studied in the literature (Rakhlin and Sridharan, 2015, Bilodeau et al., 2020, Wu et al., 2023), was shown to not characterize minimax regret in general. Inspired by the seminal work of Shtarkov (1987)
and Rakhlin, Sridharan, and Tewari (2010), we introduce a novel complexity measure, the \emph{contextual Shtarkov sum}, corresponding to the Shtarkov sum after projection onto a multiary context tree, and show that the worst case log contextual Shtarkov sum equals the minimax regret. Using the contextual Shtarkov sum, we derive the minimax optimal strategy, dubbed \emph{contextual Normalized Maximum Likelihood} (cNML). Our results hold for sequential experts, beyond binary labels, which are settings rarely considered in prior work.
To illustrate the utility of this characterization, we provide a short proof of a new regret upper bound in terms of sequential $\ell_{\infty}$ entropy, unifying and sharpening state-of-the-art bounds by Bilodeau et al. (2020) and Wu et al. (2023). | https://openreview.net/pdf/45cc567d0c06061737d622879a849ccfc81d9c05.pdf |
Multi-language Diversity Benefits Autoformalization | https://openreview.net/forum?id=2jjfRm2R6D | https://openreview.net/forum?id=2jjfRm2R6D | Albert Q. Jiang,Wenda Li,Mateja Jamnik | NIPS 2024,Poster | Autoformalization is the task of translating natural language materials into machine-verifiable formalisations. Progress in autoformalization research is hindered by the lack of a sizeable dataset consisting of informal-formal pairs expressing the same essence. Existing methods tend to circumvent this challenge by manually curating small corpora or using few-shot learning with large language models. But these methods suffer from data scarcity and formal language acquisition difficulty. In this work, we create mma, a large, flexible, multi-language, and multi-domain dataset of informal-formal pairs, by using a language model to translate in the reverse direction, that is, from formal mathematical statements into corresponding informal ones. Experiments show that language models fine-tuned on mma can produce up to $29-31$\% of statements acceptable with minimal corrections on the miniF2F and ProofNet benchmarks, up from $0$\% with the base model. We demonstrate that fine-tuning on multi-language formal data results in more capable autoformalization models even on single-language tasks. | https://openreview.net/pdf/dbbeb43c17cecc25edccd1f44c16264838e429b8.pdf |
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