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Phenomenology of Double Descent in Finite-Width Neural Networks | https://openreview.net/forum?id=lTqGXfn9Tv | https://openreview.net/forum?id=lTqGXfn9Tv | Sidak Pal Singh,Aurelien Lucchi,Thomas Hofmann,Bernhard Schölkopf | ICLR 2022,Poster | `Double descent' delineates the generalization behaviour of models depending on the regime they belong to: under- or over-parameterized. The current theoretical understanding behind the occurrence of this phenomenon is primarily based on linear and kernel regression models --- with informal parallels to neural networks via the Neural Tangent Kernel. Therefore such analyses do not adequately capture the mechanisms behind double descent in finite-width neural networks, as well as, disregard crucial components --- such as the choice of the loss function. We address these shortcomings by leveraging influence functions in order to derive suitable expressions of the population loss and its lower bound, while imposing minimal assumptions on the form of the parametric model. Our derived bounds bear an intimate connection with the spectrum of the Hessian at the optimum, and importantly, exhibit a double descent behaviour at the interpolation threshold. Building on our analysis, we further investigate how the loss function affects double descent --- and thus uncover interesting properties of neural networks and their Hessian spectra near the interpolation threshold. | https://openreview.net/pdf/692a8cdd6b0dd0b5c63c485191d55432b21ad442.pdf |
How Attentive are Graph Attention Networks? | https://openreview.net/forum?id=F72ximsx7C1 | https://openreview.net/forum?id=F72ximsx7C1 | Shaked Brody,Uri Alon,Eran Yahav | ICLR 2022,Poster | Graph Attention Networks (GATs) are one of the most popular GNN architectures and are considered as the state-of-the-art architecture for representation learning with graphs. In GAT, every node attends to its neighbors given its own representation as the query.
However, in this paper we show that GAT computes a very limited kind of attention: the ranking of the attention scores is unconditioned on the query node. We formally define this restricted kind of attention as static attention and distinguish it from a strictly more expressive dynamic attention.
Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data.
To remove this limitation, we introduce a simple fix by modifying the order of operations and propose GATv2: a dynamic graph attention variant that is strictly more expressive than GAT. We perform an extensive evaluation and show that GATv2 outperforms GAT across 12 OGB and other benchmarks while we match their parametric costs.
Our code is available at https://github.com/tech-srl/how_attentive_are_gats . GATv2 is available as part of the PyTorch Geometric library, the Deep Graph Library, and the TensorFlow GNN library. | https://openreview.net/pdf/10878ac1155ddeeada5fd384fbe0cf15747d06bf.pdf |
Learning Transferable Reward for Query Object Localization with Policy Adaptation | https://openreview.net/forum?id=92tYQiil17 | https://openreview.net/forum?id=92tYQiil17 | Tingfeng Li,Shaobo Han,Martin Renqiang Min,Dimitris N. Metaxas | ICLR 2022,Poster | We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. Our proposed method enables test-time policy adaptation to new environments where the reward signals are not readily available, and outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing the trained agent from one specific class to another class. Experiments on corrupted MNIST, CU-Birds, and COCO datasets demonstrate the effectiveness of our approach. | https://openreview.net/pdf/5b72a5cbe8d019baa19ccef469da73414589de18.pdf |
CKConv: Continuous Kernel Convolution For Sequential Data | https://openreview.net/forum?id=8FhxBtXSl0 | https://openreview.net/forum?id=8FhxBtXSl0 | David W. Romero,Anna Kuzina,Erik J Bekkers,Jakub Mikolaj Tomczak,Mark Hoogendoorn | ICLR 2022,Poster | Conventional neural architectures for sequential data present important limitations. Recurrent neural networks suffer from exploding and vanishing gradients, small effective memory horizons, and must be trained sequentially. Convolutional neural networks cannot handle sequences of unknown size and their memory horizon must be defined a priori. In this work, we show that these problems can be solved by formulating the convolutional kernels of CNNs as continuous functions. The resulting Continuous Kernel Convolution (CKConv) handles arbitrarily long sequences in a parallel manner, within a single operation, and without relying on any form of recurrence. We show that Continuous Kernel Convolutional Networks (CKCNNs) obtain state-of-the-art results in multiple datasets, e.g., permuted MNIST, and, thanks to their continuous nature, are able to handle non-uniformly sampled datasets and irregularly-sampled data natively. CKCNNs match or perform better than neural ODEs designed for these purposes in a faster and simpler manner. | https://openreview.net/pdf/eb7ec6afc6fd671f4c62e8ae61ac22465ac362ab.pdf |
Towards Empirical Sandwich Bounds on the Rate-Distortion Function | https://openreview.net/forum?id=H4PmOqSZDY | https://openreview.net/forum?id=H4PmOqSZDY | Yibo Yang,Stephan Mandt | ICLR 2022,Poster | Rate-distortion (R-D) function, a key quantity in information theory, characterizes the fundamental limit of how much a data source can be compressed subject to a fidelity criterion, by any compression algorithm. As researchers push for ever-improving compression performance, establishing the R-D function of a given data source is not only of scientific interest, but also reveals the possible room for improvement in existing compression algorithms. Previous work on this problem relied on distributional assumptions on the data source (Gibson, 2017) or only applied to discrete data (Blahut, 1972; Arimoto, 1972). By contrast, this paper makes the first attempt at an algorithm for sandwiching the R-D function of a general (not necessarily discrete) source requiring only i.i.d. data samples. We estimate R-D sandwich bounds for a variety of artificial and real-world data sources, in settings far beyond the feasibility of any known method, and shed light on the optimality of neural data compression (Ballé et al., 2021; Yang et al., 2022). Our R-D upper bound on natural images indicates theoretical room for improving state-of-the-art image compression methods by at least one dB in PSNR at various bitrates. Our data and code can be found at https://github.com/mandt-lab/RD-sandwich. | https://openreview.net/pdf/68b5094de29bac048c6f775fd6ae524a866caf89.pdf |
Pareto Policy Adaptation | https://openreview.net/forum?id=wfZGut6e09 | https://openreview.net/forum?id=wfZGut6e09 | Panagiotis Kyriakis,Jyotirmoy Deshmukh,Paul Bogdan | ICLR 2022,Poster | We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences. By enforcing Pareto stationarity, a first-order condition for Pareto optimality, we are able to design a simple policy gradient algorithm that approximates the Pareto front and infers the unknown preferences. Our method relies on a projected gradient descent solver that identifies common ascent directions for all objectives. Leveraging the solution of that solver, we introduce Pareto Policy Adaptation (PPA), a loss function that adapts the policy to be optimal with respect to any distribution over preferences. PPA uses implicit differentiation to back-propagate the loss gradient bypassing the operations of the projected gradient descent solver. Our approach is straightforward, easy to implement and can be used with all existing policy gradient and actor-critic methods. We evaluate our method in a series of reinforcement learning tasks | https://openreview.net/pdf/20c222c6c34c93ec1b453f5c07d51bd1827ec7bd.pdf |
Fair Normalizing Flows | https://openreview.net/forum?id=BrFIKuxrZE | https://openreview.net/forum?id=BrFIKuxrZE | Mislav Balunovic,Anian Ruoss,Martin Vechev | ICLR 2022,Poster | Fair representation learning is an attractive approach that promises fairness of downstream predictors by encoding sensitive data. Unfortunately, recent work has shown that strong adversarial predictors can still exhibit unfairness by recovering sensitive attributes from these representations. In this work, we present Fair Normalizing Flows (FNF), a new approach offering more rigorous fairness guarantees for learned representations. Specifically, we consider a practical setting where we can estimate the probability density for sensitive groups. The key idea is to model the encoder as a normalizing flow trained to minimize the statistical distance between the latent representations of different groups. The main advantage of FNF is that its exact likelihood computation allows us to obtain guarantees on the maximum unfairness of any potentially adversarial downstream predictor. We experimentally demonstrate the effectiveness of FNF in enforcing various group fairness notions, as well as other attractive properties such as interpretability and transfer learning, on a variety of challenging real-world datasets. | https://openreview.net/pdf/609e4c482621a5208ae4ebb3b311369c3b04689f.pdf |
The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program | https://openreview.net/forum?id=5QhUE1qiVC6 | https://openreview.net/forum?id=5QhUE1qiVC6 | Yifei Wang,Mert Pilanci | ICLR 2022,Poster | We study non-convex subgradient flows for training two-layer ReLU neural networks from a convex geometry and duality perspective. We characterize the implicit bias of unregularized non-convex gradient flow as convex regularization of an equivalent convex model. We then show that the limit points of non-convex subgradient flows can be identified via primal-dual correspondence in this convex optimization problem. Moreover, we derive a sufficient condition on the dual variables which ensures that the stationary points of the non-convex objective are the KKT points of the convex objective, thus proving convergence of non-convex gradient flows to the global optimum. For a class of regular training data distributions such as orthogonal separable data, we show that this sufficient condition holds. Therefore, non-convex gradient flows in fact converge to optimal solutions of a convex optimization problem. We present numerical results verifying the predictions of our theory for non-convex subgradient descent. | https://openreview.net/pdf/4d29755fe3cd56f6093aa8e79892bc79392b8c0d.pdf |
Adaptive Wavelet Transformer Network for 3D Shape Representation Learning | https://openreview.net/forum?id=5MLb3cLCJY | https://openreview.net/forum?id=5MLb3cLCJY | Hao Huang,Yi Fang | ICLR 2022,Poster | We present a novel method for 3D shape representation learning using multi-scale wavelet decomposition. Previous works often decompose 3D shapes into complementary components in spatial domain at a single scale. In this work, we study to decompose 3D shapes into sub-bands components in frequency domain at multiple scales, resulting in a hierarchical decomposition tree in a principled manner rooted in multi-resolution wavelet analysis. Specifically, we propose Adaptive Wavelet Transformer Network (AWT-Net) that firstly generates approximation or detail wavelet coefficients per point, classifying each point into high or low sub-bands components, using lifting scheme at multiple scales recursively and hierarchically. Then, AWT-Net exploits Transformer to enhance the original shape features by querying and fusing features from different but integrated sub-bands. The wavelet coefficients can be learned without direct supervision on coefficients, and AWT-Net is fully differentiable and can be learned in an end-to-end fashion. Extensive experiments demonstrate that AWT-Net achieves competitive performance on 3D shape classification and segmentation benchmarks. | https://openreview.net/pdf/b460e9efd8a892dfa306a2d12f830a63074ab5dd.pdf |
On the Convergence of mSGD and AdaGrad for Stochastic Optimization | https://openreview.net/forum?id=g5tANwND04i | https://openreview.net/forum?id=g5tANwND04i | ruinan Jin,Yu Xing,Xingkang He | ICLR 2022,Poster | As one of the most fundamental stochastic optimization algorithms, stochastic gradient descent (SGD) has been intensively developed and extensively applied in machine learning in the past decade. There have been some modified SGD-type algorithms, which outperform the SGD in many competitions and applications in terms of convergence rate and accuracy, such as momentum-based SGD (mSGD) and adaptive gradient algorithm (AdaGrad). Despite these empirical successes, the theoretical properties of these algorithms have not been well established due to technical difficulties. With this motivation, we focus on convergence analysis of mSGD and AdaGrad for any smooth (possibly non-convex) loss functions in stochastic optimization. First, we prove that the iterates of mSGD are asymptotically convergent to a connected set of stationary points with probability one, which is more general than existing works on subsequence convergence or convergence of time averages. Moreover, we prove that the loss function of mSGD decays at a certain rate faster than that of SGD. In addition, we prove the iterates of AdaGrad are asymptotically convergent to a connected set of stationary points with probability one. Also, this result extends the results from the literature on subsequence convergence and the convergence of time averages. Despite the generality of the above convergence results, we have relaxed some assumptions of gradient noises, convexity of loss functions, as well as boundedness of iterates. | https://openreview.net/pdf/b4ec8da613b96b5a0a6fa0fdf588173e801abfc8.pdf |
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory | https://openreview.net/forum?id=nioAdKCEdXB | https://openreview.net/forum?id=nioAdKCEdXB | Tianrong Chen,Guan-Horng Liu,Evangelos Theodorou | ICLR 2022,Poster | Schrödinger Bridge (SB) is an entropy-regularized optimal transport problem that has received increasing attention in deep generative modeling for its mathematical flexibility compared to the Scored-based Generative Model (SGM). However, it remains unclear whether the optimization principle of SB relates to the modern training of deep generative models, which often rely on constructing log-likelihood objectives.This raises questions on the suitability of SB models as a principled alternative for generative applications. In this work, we present a novel computational framework for likelihood training of SB models grounded on Forward-Backward Stochastic Differential Equations Theory – a mathematical methodology appeared in stochastic optimal control that transforms the optimality condition of SB into a set of SDEs. Crucially, these SDEs can be used to construct the likelihood objectives for SB that, surprisingly, generalizes the ones for SGM as special cases. This leads to a new optimization principle that inherits the same SB optimality yet without losing applications of modern generative training techniques, and we show that the resulting training algorithm achieves comparable results on generating realistic images on MNIST, CelebA, and CIFAR10. Our code is available at https://github.com/ghliu/SB-FBSDE. | https://openreview.net/pdf/88f4662fe55ad470a87f305792547280c33c6e1b.pdf |
Imitation Learning from Observations under Transition Model Disparity | https://openreview.net/forum?id=twv2QlJhXzo | https://openreview.net/forum?id=twv2QlJhXzo | Tanmay Gangwani,Yuan Zhou,Jian Peng | ICLR 2022,Poster | Learning to perform tasks by leveraging a dataset of expert observations, also known as imitation learning from observations (ILO), is an important paradigm for learning skills without access to the expert reward function or the expert actions. We consider ILO in the setting where the expert and the learner agents operate in different environments, with the source of the discrepancy being the transition dynamics model. Recent methods for scalable ILO utilize adversarial learning to match the state-transition distributions of the expert and the learner, an approach that becomes challenging when the dynamics are dissimilar. In this work, we propose an algorithm that trains an intermediary policy in the learner environment and uses it as a surrogate expert for the learner. The intermediary policy is learned such that the state transitions generated by it are close to the state transitions in the expert dataset. To derive a practical and scalable algorithm, we employ concepts from prior work on estimating the support of a probability distribution. Experiments using MuJoCo locomotion tasks highlight that our method compares favorably to the baselines for ILO with transition dynamics mismatch. | https://openreview.net/pdf/7fd85a0997ef411d5948afe26129e044734df91a.pdf |
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC | https://openreview.net/forum?id=4C93Qvn-tz | https://openreview.net/forum?id=4C93Qvn-tz | Erik Nijkamp,Ruiqi Gao,Pavel Sountsov,Srinivas Vasudevan,Bo Pang,Song-Chun Zhu,Ying Nian Wu | ICLR 2022,Poster | Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by deep network, is highly multi-modal in the data space. This is a serious handicap for both theory and practice of EBMs. In this paper, we propose to learn EBM with a flow-based model (or in general latent variable model) serving as a backbone, so that the EBM is a correction or an exponential tilting of the flow-based model. We show that the model has a particularly simple form in the space of the latent variables of the generative model, and MCMC sampling of the EBM in the latent space mixes well and traverses modes in the data space. This enables proper sampling and learning of EBMs. | https://openreview.net/pdf/e59fd3452037dfc60c95270a1328f0a3077b10f9.pdf |
Autonomous Learning of Object-Centric Abstractions for High-Level Planning | https://openreview.net/forum?id=rrWeE9ZDw_ | https://openreview.net/forum?id=rrWeE9ZDw_ | Steven James,Benjamin Rosman,George Konidaris | ICLR 2022,Poster | We propose a method for autonomously learning an object-centric representation of a continuous and high-dimensional environment that is suitable for planning. Such representations can immediately be transferred between tasks that share the same types of objects, resulting in agents that require fewer samples to learn a model of a new task. We first demonstrate our approach on a 2D crafting domain consisting of numerous objects where the agent learns a compact, lifted representation that generalises across objects. We then apply it to a series of Minecraft tasks to learn object-centric representations and object types - directly from pixel data - that can be leveraged to solve new tasks quickly. The resulting learned representations enable the use of a task-level planner, resulting in an agent capable of transferring learned representations to form complex, long-term plans. | https://openreview.net/pdf/a9a9310c7615055c5fd767dd5c8bde623331a28b.pdf |
A fast and accurate splitting method for optimal transport: analysis and implementation | https://openreview.net/forum?id=fCSq8yrDkc | https://openreview.net/forum?id=fCSq8yrDkc | Vien V. Mai,Jacob Lindbäck,Mikael Johansson | ICLR 2022,Poster | We develop a fast and reliable method for solving large-scale optimal transport (OT) problems at an unprecedented combination of speed and accuracy. Built on the celebrated Douglas-Rachford splitting technique, our method tackles the original OT problem directly instead of solving an approximate regularized problem, as many state-of-the-art techniques do. This allows us to provide sparse transport plans and avoid numerical issues of methods that use entropic regularization. The algorithm has the same cost per iteration as the popular Sinkhorn method, and each iteration can be executed efficiently, in parallel. The proposed method enjoys an iteration complexity $O(1/\epsilon)$ compared to the best-known $O(1/\epsilon^2)$ of the Sinkhorn method. In addition, we establish a linear convergence rate for our formulation of the OT problem. We detail an efficient GPU implementation of the proposed method that maintains a primal-dual stopping criterion at no extra cost. Substantial experiments demonstrate the effectiveness of our method, both in terms of computation times and robustness. | https://openreview.net/pdf/c466eee883ed6f6c6b6dad8f7dc4d5f36092bb25.pdf |
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks | https://openreview.net/forum?id=VLgmhQDVBV | https://openreview.net/forum?id=VLgmhQDVBV | Benjamin Bowman,Guido Montufar | ICLR 2022,Poster | We study the dynamics of a neural network in function space when optimizing the mean squared error via gradient flow. We show that in the underparameterized regime the network learns eigenfunctions of an integral operator $T_K$ determined by the Neural Tangent Kernel at rates corresponding to their eigenvalues. For example, for uniformly distributed data on the sphere $S^{d - 1}$ and rotation invariant weight distributions, the eigenfunctions of $T_K$ are the spherical harmonics. Our results can be understood as describing a spectral bias in the underparameterized regime. The proofs use the concept of ``Damped Deviations'' where deviations of the NTK matter less for eigendirections with large eigenvalues. Aside from the underparameterized regime, the damped deviations point-of-view allows us to extend certain results in the literature in the overparameterized setting. | https://openreview.net/pdf/ccf082acfc5365ae3951682ff517392f46042ab1.pdf |
Discovering Latent Concepts Learned in BERT | https://openreview.net/forum?id=POTMtpYI1xH | https://openreview.net/forum?id=POTMtpYI1xH | Fahim Dalvi,Abdul Rafae Khan,Firoj Alam,Nadir Durrani,Jia Xu,Hassan Sajjad | ICLR 2022,Poster | A large number of studies that analyze deep neural network models and their ability to encode various linguistic and non-linguistic concepts provide an interpretation of the inner mechanics of these models. The scope of the analyses is limited to pre-defined concepts that reinforce the traditional linguistic knowledge and do not reflect on how novel concepts are learned by the model. We address this limitation by discovering and analyzing latent concepts learned in neural network models in an unsupervised fashion and provide interpretations from the model's perspective. In this work, we study: i) what latent concepts exist in the pre-trained BERT model, ii) how the discovered latent concepts align or diverge from classical linguistic hierarchy and iii) how the latent concepts evolve across layers.
Our findings show: i) a model learns novel concepts (e.g. animal categories and demographic groups), which do not strictly adhere to any pre-defined categorization (e.g. POS, semantic tags), ii) several latent concepts are based on multiple properties which may include semantics, syntax, and morphology, iii) the lower layers in the model dominate in learning shallow lexical concepts while the higher layers learn semantic relations and iv) the discovered latent concepts highlight potential biases learned in the model. We also release a novel BERT ConceptNet dataset consisting of 174 concept labels and 1M annotated instances. | https://openreview.net/pdf/f96833144308f5ae7999c4c5e5f0dc8c6208fe67.pdf |
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks | https://openreview.net/forum?id=dNigytemkL | https://openreview.net/forum?id=dNigytemkL | Rahim Entezari,Hanie Sedghi,Olga Saukh,Behnam Neyshabur | ICLR 2022,Poster | In this paper, we conjecture that if the permutation invariance of neural networks is taken into account, SGD solutions will likely have no barrier in the linear interpolation between them. Although it is a bold conjecture, we show how extensive empirical attempts fall short of refuting it. We further provide a preliminary theoretical result to support our conjecture. Our conjecture has implications for the lottery ticket hypothesis, distributed training, and ensemble methods. The source code is available at \url{https://github.com/rahimentezari/PermutationInvariance}. | https://openreview.net/pdf/a575dfe6923ea65c17895fd63d13fc299132536f.pdf |
Data Poisoning Won’t Save You From Facial Recognition | https://openreview.net/forum?id=B5XahNLmna | https://openreview.net/forum?id=B5XahNLmna | Evani Radiya-Dixit,Sanghyun Hong,Nicholas Carlini,Florian Tramer | ICLR 2022,Poster | Data poisoning has been proposed as a compelling defense against facial recognition models trained on Web-scraped pictures. Users can perturb images they post online, so that models will misclassify future (unperturbed) pictures.
We demonstrate that this strategy provides a false sense of security, as it ignores an inherent asymmetry between the parties: users' pictures are perturbed once and for all before being published (at which point they are scraped) and must thereafter fool all future models---including models trained adaptively against the users' past attacks, or models that use new technologies discovered after the attack.
We evaluate two systems for poisoning attacks against large-scale facial recognition, Fawkes (500,000+ downloads) and LowKey. We demonstrate how an "oblivious" model trainer can simply wait for future developments in computer vision to nullify the protection of pictures collected in the past. We further show that an adversary with black-box access to the attack can (i) train a robust model that resists the perturbations of collected pictures and (ii) detect poisoned pictures uploaded online.
We caution that facial recognition poisoning will not admit an "arms race" between attackers and defenders. Once perturbed pictures are scraped, the attack cannot be changed so any future successful defense irrevocably undermines users' privacy. | https://openreview.net/pdf/664ff8f2d58700ae00821a00773fdedbf383c737.pdf |
MetaMorph: Learning Universal Controllers with Transformers | https://openreview.net/forum?id=Opmqtk_GvYL | https://openreview.net/forum?id=Opmqtk_GvYL | Agrim Gupta,Linxi Fan,Surya Ganguli,Li Fei-Fei | ICLR 2022,Poster | Multiple domains like vision, natural language, and audio are witnessing tremendous progress by leveraging Transformers for large scale pre-training followed by task specific fine tuning. In contrast, in robotics we primarily train a single robot for a single task. However, modular robot systems now allow for the flexible combination of general-purpose building blocks into task optimized morphologies. However, given the exponentially large number of possible robot morphologies, training a controller for each new design is impractical. In this work, we propose MetaMorph, a Transformer based approach to learn a universal controller over a modular robot design space. MetaMorph is based on the insight that robot morphology is just another modality on which we can condition the output of a Transformer. Through extensive experiments we demonstrate that large scale pre-training on a variety of robot morphologies results in policies with combinatorial generalization capabilities, including zero shot generalization to unseen robot morphologies. We further demonstrate that our pre-trained policy can be used for sample-efficient transfer to completely new robot morphologies and tasks. | https://openreview.net/pdf/7ef00fd81bdb696532e182f6073e6e6d9cb15e98.pdf |
HTLM: Hyper-Text Pre-Training and Prompting of Language Models | https://openreview.net/forum?id=P-pPW1nxf1r | https://openreview.net/forum?id=P-pPW1nxf1r | Armen Aghajanyan,Dmytro Okhonko,Mike Lewis,Mandar Joshi,Hu Xu,Gargi Ghosh,Luke Zettlemoyer | ICLR 2022,Poster | We introduce HTLM, a hyper-text language model trained on a large-scale web crawl. Modeling hyper-text has a number of advantages: (1) it is easily gathered at scale, (2) it provides rich document-level and end-task-adjacent supervision (e.g. 'class' and 'id' attributes often encode document category information), and (3) it allows for new structured prompting that follows the established semantics of HTML (e.g. to do zero-shot summarization by infilling '<title>' tags for a webpage that contains the input text). We show that pretraining with a BART-style denoising loss directly on simplified HTML provides highly effective transfer for a wide range of end tasks and supervision levels. HTLM matches or exceeds the performance of comparably sized text-only LMs for zero-shot prompting and fine-tuning for classification benchmarks, while also setting new state-of-the-art performance levels for zero-shot summarization. We also find that hyper-text prompts provide more value to HTLM, in terms of data efficiency, than plain text prompts do for existing LMs, and that HTLM is highly effective at auto-prompting itself, by simply generating the most likely hyper-text formatting for any available training data. We will release all code and models to support future HTLM research. | https://openreview.net/pdf/565914339be7ddb2453523852e836e1fe3ce3b8b.pdf |
Illiterate DALL-E Learns to Compose | https://openreview.net/forum?id=h0OYV0We3oh | https://openreview.net/forum?id=h0OYV0We3oh | Gautam Singh,Fei Deng,Sungjin Ahn | ICLR 2022,Poster | Although DALL-E has shown an impressive ability of composition-based systematic generalization in image generation, it requires the dataset of text-image pairs and the compositionality is provided by the text. In contrast, object-centric representation models like the Slot Attention model learn composable representations without the text prompt. However, unlike DALL-E, its ability to systematically generalize for zero-shot generation is significantly limited. In this paper, we propose a simple but novel slot-based autoencoding architecture, called SLATE, for combining the best of both worlds: learning object-centric representations that allow systematic generalization in zero-shot image generation without text. As such, this model can also be seen as an illiterate DALL-E model. Unlike the pixel-mixture decoders of existing object-centric representation models, we propose to use the Image GPT decoder conditioned on the slots for capturing complex interactions among the slots and pixels. In experiments, we show that this simple and easy-to-implement architecture not requiring a text prompt achieves significant improvement in in-distribution and out-of-distribution (zero-shot) image generation and qualitatively comparable or better slot-attention structure than the models based on mixture decoders. | https://openreview.net/pdf/bcb5e847c6cefafd402be4829f49227cf6b457c7.pdf |
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models | https://openreview.net/forum?id=JYtwGwIL7ye | https://openreview.net/forum?id=JYtwGwIL7ye | Alexander Pan,Kush Bhatia,Jacob Steinhardt | ICLR 2022,Poster | Reward hacking---where RL agents exploit gaps in misspecified proxy rewards---has been widely observed, but not yet systematically studied. To understand reward hacking, we construct four RL environments with different misspecified rewards. We investigate reward hacking as a function of agent capabilities: model capacity, action space resolution, and observation space noise. Typically, more capable agents are able to better exploit reward misspecifications, causing them to attain higher proxy reward and lower true reward. Moreover, we find instances of \emph{phase transitions}: capability thresholds at which the agent's behavior qualitatively shifts, leading to a sharp decrease in the true reward. Such phase transitions pose challenges to monitoring the safety of ML systems. To encourage further research on reward misspecification, address this, we propose an anomaly detection task for aberrant policies and offer several baseline detectors. | https://openreview.net/pdf/772b3ddc867fd13a9c98fb15c99f94d5b68ae558.pdf |
Optimizing Neural Networks with Gradient Lexicase Selection | https://openreview.net/forum?id=J_2xNmVcY4 | https://openreview.net/forum?id=J_2xNmVcY4 | Li Ding,Lee Spector | ICLR 2022,Poster | One potential drawback of using aggregated performance measurement in machine learning is that models may learn to accept higher errors on some training cases as compromises for lower errors on others, with the lower errors actually being instances of overfitting. This can lead both to stagnation at local optima and to poor generalization. Lexicase selection is an uncompromising method developed in evolutionary computation, which selects models on the basis of sequences of individual training case errors instead of using aggregated metrics such as loss and accuracy. In this paper, we investigate how the general idea of lexicase selection can fit into the context of deep learning to improve generalization. We propose Gradient Lexicase Selection, an optimization framework that combines gradient descent and lexicase selection in an evolutionary fashion. Experimental results show that the proposed method improves the generalization performance of various popular deep neural network architectures on three image classification benchmarks. Qualitative analysis also indicates that our method helps the networks learn more diverse representations. | https://openreview.net/pdf/769eeb9706c07b44bcffed8ceeb0c8f027af3caf.pdf |
Offline Reinforcement Learning with Implicit Q-Learning | https://openreview.net/forum?id=68n2s9ZJWF8 | https://openreview.net/forum?id=68n2s9ZJWF8 | Ilya Kostrikov,Ashvin Nair,Sergey Levine | ICLR 2022,Poster | Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This tradeoff is critical, because most current offline reinforcement learning methods need to query the value of unseen actions during training to improve the policy, and therefore need to either constrain these actions to be in-distribution, or else regularize their values. We propose a new offline RL method that never needs to evaluate actions outside of the dataset, but still enables the learned policy to improve substantially over the best behavior in the data through generalization. The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state. This leverages the generalization capacity of the function approximator to estimate the value of the best available action at a given state without ever directly querying a Q-function with this unseen action. Our algorithm alternates between fitting this upper expectile value function and backing it up into a Q-function, without any explicit policy. Then, we extract the policy via advantage-weighted behavioral cloning, which also avoids querying out-of-sample actions. We dub our method Implicit Q-learning (IQL). IQL is easy to implement, computationally efficient, and only requires fitting an additional critic with an asymmetric L2 loss. IQL demonstrates the state-of-the-art performance on D4RL, a standard benchmark for offline reinforcement learning. We also demonstrate that IQL achieves strong performance fine-tuning using online interaction after offline initialization. | https://openreview.net/pdf/40ae9547c1a21998c591513e30da199c204553ac.pdf |
Learning Distributionally Robust Models at Scale via Composite Optimization | https://openreview.net/forum?id=To-R742x7se | https://openreview.net/forum?id=To-R742x7se | Farzin Haddadpour,Mohammad Mahdi Kamani,Mehrdad Mahdavi,amin karbasi | ICLR 2022,Poster | To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existing approaches to learning a distributionally robust model either require solving complex optimization problems such as semidefinite programming or a first-order method whose convergence scales linearly with the number of data samples-- which hinders their scalability to large datasets. In this paper, we show how different variants of DRO are simply instances of a finite-sum composite optimization for which we provide scalable methods. We also provide empirical results that demonstrate the effectiveness of our proposed algorithm with respect to the prior art in order to learn robust models from very large datasets. | https://openreview.net/pdf/df930de07a8cc14a26729682ca5b2e909c56cfbd.pdf |
Counterfactual Plans under Distributional Ambiguity | https://openreview.net/forum?id=noaG7SrPVK0 | https://openreview.net/forum?id=noaG7SrPVK0 | Ngoc Bui,Duy Nguyen,Viet Anh Nguyen | ICLR 2022,Poster | Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains. A counterfactual plan consists of multiple possibilities to modify a given instance so that the model's prediction will be altered. As the predictive model can be updated subject to the future arrival of new data, a counterfactual plan may become ineffective or infeasible, with respect to the future values of the model parameters. In this work, we study the counterfactual plans under model uncertainty, in which the distribution of the model parameters is partially prescribed using only the first- and second-moment information. First, we propose an uncertainty quantification tool to compute the lower and upper bounds of the probability of feasibility for any given counterfactual plan. We then provide corrective methods to adjust the counterfactual plan to improve the feasibility measure. The numerical experiments validate our bounds and demonstrate that our correction increases the robustness of the counterfactual plans in different real-world datasets. | https://openreview.net/pdf/c3f5f4075890ebfe659f86fbf42ec54a78759f3f.pdf |
Neural Parameter Allocation Search | https://openreview.net/forum?id=srtIXtySfT4 | https://openreview.net/forum?id=srtIXtySfT4 | Bryan A. Plummer,Nikoli Dryden,Julius Frost,Torsten Hoefler,Kate Saenko | ICLR 2022,Poster | Training neural networks requires increasing amounts of memory. Parameter sharing can reduce memory and communication costs, but existing methods assume networks have many identical layers and utilize hand-crafted sharing strategies that fail to generalize. We introduce Neural Parameter Allocation Search (NPAS), a novel task where the goal is to train a neural network given an arbitrary, fixed parameter budget. NPAS covers both low-budget regimes, which produce compact networks, as well as a novel high-budget regime, where additional capacity can be added to boost performance without increasing inference FLOPs. To address NPAS, we introduce Shapeshifter Networks (SSNs), which automatically learn where and how to share parameters in a network to support any parameter budget without requiring any changes to the architecture or loss function. NPAS and SSNs provide a complete framework for addressing generalized parameter sharing, and can also be combined with prior work for additional performance gains. We demonstrate the effectiveness of our approach using nine network architectures across four diverse tasks, including ImageNet classification and transformers. | https://openreview.net/pdf/41098cac197bacfbb30bd46f809e88c6d1cf5d76.pdf |
Non-Linear Operator Approximations for Initial Value Problems | https://openreview.net/forum?id=d2TT6gK9qZn | https://openreview.net/forum?id=d2TT6gK9qZn | Gaurav Gupta,Xiongye Xiao,Radu Balan,Paul Bogdan | ICLR 2022,Poster | Time-evolution of partial differential equations is the key to model several dynamical processes, events forecasting but the operators associated with such problems are non-linear. We propose a Padé approximation based exponential neural operator scheme for efficiently learning the map between a given initial condition and activities at a later time. The multiwavelets bases are used for space discretization. By explicitly embedding the exponential operators in the model, we reduce the training parameters and make it more data-efficient which is essential in dealing with scarce real-world datasets. The Padé exponential operator uses a $\textit{recurrent structure with shared parameters}$ to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession. We show theoretically that the gradients associated with the recurrent Padé network are bounded across the recurrent horizon. We perform experiments on non-linear systems such as Korteweg-de Vries (KdV) and Kuramoto–Sivashinsky (KS) equations to show that the proposed approach achieves the best performance and at the same time is data-efficient. We also show that urgent real-world problems like Epidemic forecasting (for example, COVID-19) can be formulated as a 2D time-varying operator problem. The proposed Padé exponential operators yield better prediction results ($\textbf{53\%} (\textbf{52\%})$ better MAE than best neural operator (non-neural operator deep learning model)) compared to state-of-the-art forecasting models. | https://openreview.net/pdf/64034193e19bb69648d9e3ed9301e225d08d08fe.pdf |
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates | https://openreview.net/forum?id=7IWGzQ6gZ1D | https://openreview.net/forum?id=7IWGzQ6gZ1D | Safa Alver,Doina Precup | ICLR 2022,Poster | We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforcement learning tasks with no or very little new data. Specifically, we consider the framework of generalized policy evaluation and improvement, in which the rewards for all tasks of interest are assumed to be expressible as a linear combination of a fixed set of features. We show theoretically that, under certain assumptions, having access to a specific set of diverse policies, which we call a set of independent policies, can allow for instantaneously achieving high-level performance on all possible downstream tasks which are typically more complex than the ones on which the agent was trained. Based on this theoretical analysis, we propose a simple algorithm that iteratively constructs this set of policies. In addition to empirically validating our theoretical results, we compare our approach with recently proposed diverse policy set construction methods and show that, while others fail, our approach is able to build a behavior basis that enables instantaneous transfer to all possible downstream tasks. We also show empirically that having access to a set of independent policies can better bootstrap the learning process on downstream tasks where the new reward function cannot be described as a linear combination of the features. Finally, we demonstrate how this policy set can be useful in a lifelong reinforcement learning setting. | https://openreview.net/pdf/99cd11d061fb209a0dffb177bc34b326b5d4d7a6.pdf |
Collapse by Conditioning: Training Class-conditional GANs with Limited Data | https://openreview.net/forum?id=7TZeCsNOUB_ | https://openreview.net/forum?id=7TZeCsNOUB_ | Mohamad Shahbazi,Martin Danelljan,Danda Pani Paudel,Luc Van Gool | ICLR 2022,Poster | Class-conditioning offers a direct means to control a Generative Adversarial Network (GAN) based on a discrete input variable. While necessary in many applications, the additional information provided by the class labels could even be expected to benefit the training of the GAN itself. On the contrary, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability. Motivated by this observation, we propose a training strategy for class-conditional GANs (cGANs) that effectively prevents the observed mode-collapse by leveraging unconditional learning. Our training strategy starts with an unconditional GAN and gradually injects the class conditioning into the generator and the objective function. The proposed method for training cGANs with limited data results not only in stable training but also in generating high-quality images, thanks to the early-stage exploitation of the shared information across classes. We analyze the observed mode collapse problem in comprehensive experiments on four datasets. Our approach demonstrates outstanding results compared with state-of-the-art methods and established baselines. The code is available at https://github.com/mshahbazi72/transitional-cGAN | https://openreview.net/pdf/3fccdf773fc5217d7e02e135a75df9f5ac0e7de9.pdf |
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize | https://openreview.net/forum?id=dSw0QtRMJkO | https://openreview.net/forum?id=dSw0QtRMJkO | Ali Kavis,Kfir Yehuda Levy,Volkan Cevher | ICLR 2022,Poster | In this paper, we propose a new, simplified high probability analysis of AdaGrad for smooth, non-convex problems.
More specifically, we focus on a particular accelerated gradient (AGD) template (Lan, 2020), through which we recover the original AdaGrad and its variant with averaging, and prove a convergence rate of $\mathcal O (1/ \sqrt{T})$ with high probability without the knowledge of smoothness and variance.
We use a particular version of Freedman's concentration bound for martingale difference sequences (Kakade & Tewari, 2008) which enables us to achieve the best-known dependence of $\log (1 / \delta )$ on the probability margin $\delta$.
We present our analysis in a modular way and obtain a complementary $\mathcal O (1 / T)$ convergence rate in the deterministic setting.
To the best of our knowledge, this is the first high probability result for AdaGrad with a truly adaptive scheme, i.e., completely oblivious to the knowledge of smoothness and uniform variance bound, which simultaneously has best-known dependence of $\log( 1/ \delta)$.
We further prove noise adaptation property of AdaGrad under additional noise assumptions. | https://openreview.net/pdf/11cf98dd0d3e5fe19f0cfc472913b4778244ba26.pdf |
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments | https://openreview.net/forum?id=1NUsBU-7HAL | https://openreview.net/forum?id=1NUsBU-7HAL | Sugandha Sharma,Aidan Curtis,Marta Kryven,Joshua B. Tenenbaum,Ila R Fiete | ICLR 2022,Poster | Humans are expert explorers and foragers. Understanding the computational cognitive mechanisms that support this capability can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that humans explore new environments by inferring the structure of unobserved spaces through re-use of spatial information collected from previously explored spaces. Taking inspiration from the neuroscience of repeating map fragments and ideas about program induction, we present a novel ``Map Induction'' framework, which involves the generation of novel map proposals for unseen environments based on compositions of already-seen spaces in a Hierarchical Bayesian framework. The model thus explicitly reasons about unseen spaces through a distribution of strong spatial priors. We introduce a new behavioral Map Induction Task (MIT) that involves foraging for rewards to compare human performance with state-of-the-art existing models and Map Induction. We show that Map Induction better predicts human behavior than the non-inductive baselines. We also show that Map Induction, when used to augment state-of-the-art approximate planning algorithms, improves their performance.
| https://openreview.net/pdf/e711160279e8d24176f9ee29e77240aab6849830.pdf |
How Did the Model Change? Efficiently Assessing Machine Learning API Shifts | https://openreview.net/forum?id=gFDFKC4gHL4 | https://openreview.net/forum?id=gFDFKC4gHL4 | Lingjiao Chen,Matei Zaharia,James Zou | ICLR 2022,Poster | ML prediction APIs from providers like Amazon and Google have made it simple to use ML in applications. A challenge for users is that such APIs continuously change over time as the providers update models, and changes can happen silently without users knowing. It is thus important to monitor when and how much the MLAPIs’ performance shifts. To provide detailed change assessment, we model MLAPI shifts as confusion matrix differences, and propose a principled algorithmic framework, MASA, to provably assess these shifts efficiently given a sample budget constraint.MASAemploys an upper-confidence bound based approach to adaptively determine on which data point to query the ML API to estimate shifts. Empirically, we observe significant ML API shifts from 2020 to 2021 among 12 out of 36 applications using commercial APIs from Google, Microsoft, Amazon, and other providers. These real-world shifts include both improvements and reductions in accuracy. Extensive experiments show that MASA can estimate such API shifts more accurately than standard approaches given the same budget | https://openreview.net/pdf/acadda3ff8e7686ab4e0b73fd64ba0ef89f2134a.pdf |
A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model | https://openreview.net/forum?id=31d5RLCUuXC | https://openreview.net/forum?id=31d5RLCUuXC | Jianwen Xie,Yaxuan Zhu,Jun Li,Ping Li | ICLR 2022,Poster | This paper studies the cooperative learning of two generative flow models, in which the two models are iteratively updated based on the jointly synthesized examples. The first flow model is a normalizing flow that transforms an initial simple density to a target density by applying a sequence of invertible transformations. The second flow model is a Langevin flow that runs finite steps of gradient-based MCMC toward an energy-based model. We start from proposing a generative framework that trains an energy-based model with a normalizing flow as an amortized sampler to initialize the MCMC chains of the energy-based model. In each learning iteration, we generate synthesized examples by using a normalizing flow initialization followed by a short-run Langevin flow revision toward the current energy-based model. Then we treat the synthesized examples as fair samples from the energy-based model and update the model parameters with the maximum likelihood learning gradient, while the normalizing flow directly learns from the synthesized examples by maximizing the tractable likelihood. Under the short-run non-mixing MCMC scenario, the estimation of the energy-based model is shown to follow the perturbation of maximum likelihood, and the short-run Langevin flow and the normalizing flow form a two-flow generator that we call CoopFlow. We provide an understating of the CoopFlow algorithm by information geometry and show that it is a valid generator as it converges to a moment matching estimator. We demonstrate that the trained CoopFlow is capable of synthesizing realistic images, reconstructing images, and interpolating between images. | https://openreview.net/pdf/64ee4e9d385df72d0c9c8052fdd6ef8cc2b78e7a.pdf |
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? | https://openreview.net/forum?id=WVX0NNVBBkV | https://openreview.net/forum?id=WVX0NNVBBkV | Vikash Sehwag,Saeed Mahloujifar,Tinashe Handina,Sihui Dai,Chong Xiang,Mung Chiang,Prateek Mittal | ICLR 2022,Poster | While additional training data improves the robustness of deep neural networks against adversarial examples, it presents the challenge of curating a large number of specific real-world samples. We circumvent this challenge by using additional data from proxy distributions learned by advanced generative models. We first seek to formally understand the transfer of robustness from classifiers trained on proxy distributions to the real data distribution. We prove that the difference between the robustness of a classifier on the two distributions is upper bounded by the conditional Wasserstein distance between them. Next we use proxy distributions to significantly improve the performance of adversarial training on five different datasets. For example, we improve robust accuracy by up to $7.5$% and $6.7$% in $\ell_{\infty}$ and $\ell_2$ threat model over baselines that are not using proxy distributions on the CIFAR-10 dataset. We also improve certified robust accuracy by $7.6$% on the CIFAR-10 dataset. We further demonstrate that different generative models brings a disparate improvement in the performance in robust training. We propose a robust discrimination approach to characterize the impact and further provide a deeper understanding of why diffusion-based generative models are a better choice for proxy distribution than generative adversarial networks. | https://openreview.net/pdf/de688f13dda6db1dc9ca524f5de9b302f9b5261d.pdf |
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap | https://openreview.net/forum?id=ECvgmYVyeUz | https://openreview.net/forum?id=ECvgmYVyeUz | Yifei Wang,Qi Zhang,Yisen Wang,Jiansheng Yang,Zhouchen Lin | ICLR 2022,Poster | Recently, contrastive learning has risen to be a promising approach for large-scale self-supervised learning. However, theoretical understanding of how it works is still unclear. In this paper, we propose a new guarantee on the downstream performance without resorting to the conditional independence assumption that is widely adopted in previous work but hardly holds in practice. Our new theory hinges on the insight that the support of different intra-class samples will become more overlapped under aggressive data augmentations, thus simply aligning the positive samples (augmented views of the same sample) could make contrastive learning cluster intra-class samples together. Based on this augmentation overlap perspective, theoretically, we obtain asymptotically closed bounds for downstream performance under weaker assumptions, and empirically, we propose an unsupervised model selection metric ARC that aligns well with downstream accuracy. Our theory suggests an alternative understanding of contrastive learning: the role of aligning positive samples is more like a surrogate task than an ultimate goal, and the overlapped augmented views (i.e., the chaos) create a ladder for contrastive learning to gradually learn class-separated representations. The code for computing ARC is available at https://github.com/zhangq327/ARC. | https://openreview.net/pdf/69ce3c69488d43f1e8d28d5759b1cd8a9c731519.pdf |
Language-biased image classification: evaluation based on semantic representations | https://openreview.net/forum?id=xNO7OEIcJc6 | https://openreview.net/forum?id=xNO7OEIcJc6 | Yoann Lemesle,Masataka Sawayama,Guillermo Valle-Perez,Maxime Adolphe,Hélène Sauzéon,Pierre-Yves Oudeyer | ICLR 2022,Poster | Humans show language-biased image recognition for a word-embedded image, known as picture-word interference. Such interference depends on hierarchical semantic categories and reflects that human language processing highly interacts with visual processing. Similar to humans, recent artificial models jointly trained on texts and images, e.g., OpenAI CLIP, show language-biased image classification. Exploring whether the bias leads to interference similar to those observed in humans can contribute to understanding how much the model acquires hierarchical semantic representations from joint learning of language and vision. The present study introduces methodological tools from the cognitive science literature to assess the biases of artificial models. Specifically, we introduce a benchmark task to test whether words superimposed on images can distort the image classification across different category levels and, if it can, whether the perturbation is due to the shared semantic representation between language and vision. Our dataset is a set of word-embedded images and consists of a mixture of natural image datasets and hierarchical word labels with superordinate/basic category levels. Using this benchmark test, we evaluate the CLIP model. We show that presenting words distorts the image classification by the model across different category levels, but the effect does not depend on the semantic relationship between images and embedded words. This suggests that the semantic word representation in the CLIP visual processing is not shared with the image representation, although the word representation strongly dominates for word-embedded images. | https://openreview.net/pdf/b094eb383e555c4e4fa0c02dc8416aabb50bcc00.pdf |
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models | https://openreview.net/forum?id=fwzUgo0FM9v | https://openreview.net/forum?id=fwzUgo0FM9v | Liam H Fowl,Jonas Geiping,Wojciech Czaja,Micah Goldblum,Tom Goldstein | ICLR 2022,Poster | Federated learning has quickly gained popularity with its promises of increased user privacy and efficiency. Previous works have shown that federated gradient updates contain information that can be used to approximately recover user data in some situations. These previous attacks on user privacy have been limited in scope and do not scale to gradient updates aggregated over even a handful of data points, leaving some to conclude that data privacy is still intact for realistic training regimes. In this work, we introduce a new threat model based on minimal but malicious modifications of the shared model architecture which enable the server to directly obtain a verbatim copy of user data from gradient updates without solving difficult inverse problems. Even user data aggregated over large batches – where previous methods fail to extract meaningful content – can be reconstructed by these minimally modified models.
| https://openreview.net/pdf/f001474d4a74672028ea06af6f68b4e7c71bca47.pdf |
Practical Conditional Neural Process Via Tractable Dependent Predictions | https://openreview.net/forum?id=3pugbNqOh5m | https://openreview.net/forum?id=3pugbNqOh5m | Stratis Markou,James Requeima,Wessel Bruinsma,Anna Vaughan,Richard E Turner | ICLR 2022,Poster | Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and naturally handle off-the-grid and missing data. CNPs scale to large datasets and train with ease. Due to these features, CNPs appear well-suited to tasks from environmental sciences or healthcare. Unfortunately, CNPs do not produce correlated predictions, making them fundamentally inappropriate for many estimation and decision making tasks. Predicting heat waves or floods, for example, requires modelling dependencies in temperature or precipitation over time and space. Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018b) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive. What is needed is an approach which provides dependent predictions, but is simple to train and computationally tractable. In this work, we present a new class of Neural Process models that make correlated predictions and support exact maximum likelihood training that is simple and scalable. We extend the proposed models by using invertible output transformations, to capture non-Gaussian output distributions. Our models can be used in downstream estimation tasks which require dependent function samples. By accounting for output dependencies, our models show improved predictive performance on a range of experiments with synthetic and real data. | https://openreview.net/pdf/55352f18a5f52efe2957b212d6bdcf7771277fd7.pdf |
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning | https://openreview.net/forum?id=OWZVD-l-ZrC | https://openreview.net/forum?id=OWZVD-l-ZrC | Xinran Liang,Katherine Shu,Kimin Lee,Pieter Abbeel | ICLR 2022,Poster | Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by actively incorporating human feedback, i.e. teacher's preferences between two clips of behaviors. However, poor feedback-efficiency still remains as a problem in current preference-based RL algorithms, as tailored human feedback is very expensive. To handle this issue, previous methods have mainly focused on improving query selection and policy initialization. At the same time, recent exploration methods have proven to be a recipe for improving sample-efficiency in RL. We present an exploration method specifically for preference-based RL algorithms. Our main idea is to design an intrinsic reward by measuring the novelty based on learned reward. Specifically, we utilize disagreement across ensemble of learned reward models. Our intuition is that disagreement in learned reward model reflects uncertainty in tailored human feedback and could be useful for exploration. Our experiments show that reward uncertainty exploration improves both feedback- and sample-efficiency of preference-based RL algorithms on complex robot manipulation tasks from Meta-World benchmarks, compared with other existing exploration methods that measure the novelty of state visitation. | https://openreview.net/pdf/96609cdcc6871a3da572808bab08da66cc38cb2c.pdf |
Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach | https://openreview.net/forum?id=oj2yn1Q4Ett | https://openreview.net/forum?id=oj2yn1Q4Ett | Prashant Khanduri,Haibo Yang,Mingyi Hong,Jia Liu,Hoi To Wai,Sijia Liu | ICLR 2022,Poster | This work develops a novel framework for communication-efficient distributed learning where the models to be learned are overparameterized. We focus on a class of kernel learning problems (which includes the popular neural tangent kernel (NTK) learning as a special case) and propose a novel {\it multi-agent kernel approximation} technique that allows the agents to distributedly estimate the full kernel function, and subsequently perform decentralized optimization, without directly exchanging any local data or parameters. The proposed framework is a significant departure from the classical consensus-based approaches, because the agents do not exchange problem parameters, and no consensus is required. We analyze the optimization and the generalization performance of the proposed framework for the $\ell_2$ loss. We show that with $M$ agents and $N$ total samples when certain generalized inner-product kernels (resp. the random features kernel) are used, each agent needs to communicate $\mathcal{O}\big({N^2}/{M}\big)$ bits (resp. $\mathcal{O}\big(N \sqrt{N}/M \big)$ real values) to achieve minimax optimal generalization performance. We validate the theoretical results on 90 UCI benchmarking datasets (with average data size $N \approx 1000$) and show that each agent needs to share a total of $200N/M$ bits (resp. $3N/M$ real values) to closely match the performance of the centralized algorithms, and these numbers are independent of parameter and feature dimensions. | https://openreview.net/pdf/d2d0106daf104b8b9c7f3868ca69a3a81cd09902.pdf |
Permutation-Based SGD: Is Random Optimal? | https://openreview.net/forum?id=YiBa9HKTyXE | https://openreview.net/forum?id=YiBa9HKTyXE | Shashank Rajput,Kangwook Lee,Dimitris Papailiopoulos | ICLR 2022,Poster | A recent line of ground-breaking results for permutation-based SGD has corroborated a widely observed phenomenon: random permutations offer faster convergence than with-replacement sampling. However, is random optimal? We show that this depends heavily on what functions we are optimizing, and the convergence gap between optimal and random permutations can vary from exponential to nonexistent. We first show that for 1-dimensional strongly convex functions, with smooth second derivatives, there exist optimal permutations that offer exponentially faster convergence compared to random. However, for general strongly convex functions, random permutations are optimal. Finally, we show that for quadratic, strongly-convex functions, there are easy-to-construct permutations that lead to accelerated convergence compared to random. Our results suggest that a general convergence characterization of optimal permutations cannot capture the nuances of individual function classes, and can mistakenly indicate that one cannot do much better than random. | https://openreview.net/pdf/38cb93486fb7896b831a82484bdf43c375bd5b9e.pdf |
Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation | https://openreview.net/forum?id=4p6_5HBWPCw | https://openreview.net/forum?id=4p6_5HBWPCw | Shichang Zhang,Yozen Liu,Yizhou Sun,Neil Shah | ICLR 2022,Poster | Graph Neural Networks (GNNs) are popular for graph machine learning and have shown great results on wide node classification tasks. Yet, they are less popular for practical deployments in the industry owing to their scalability challenges incurred by data dependency. Namely, GNN inference depends on neighbor nodes multiple hops away from the target, and fetching them burdens latency-constrained applications. Existing inference acceleration methods like pruning and quantization can speed up GNNs by reducing Multiplication-and-ACcumulation (MAC) operations, but the improvements are limited given the data dependency is not resolved. Conversely, multi-layer perceptrons (MLPs) have no graph dependency and infer much faster than GNNs, even though they are less accurate than GNNs for node classification in general. Motivated by these complementary strengths and weaknesses, we bring GNNs and MLPs together via knowledge distillation (KD). Our work shows that the performance of MLPs can be improved by large margins with GNN KD. We call the distilled MLPs Graph-less Neural Networks (GLNNs) as they have no inference graph dependency. We show that GLNNs with competitive accuracy infer faster than GNNs by 146X-273X and faster than other acceleration methods by 14X-27X. Under a production setting involving both transductive and inductive predictions across 7 datasets, GLNN accuracies improve over stand-alone MLPs by 12.36% on average and match GNNs on 6/7 datasets. Comprehensive analysis shows when and why GLNNs can achieve competitive accuracies to GNNs and suggests GLNN as a handy choice for latency-constrained applications. | https://openreview.net/pdf/96f207794ab92fa350a4284d8d40ad874c3d9d06.pdf |
Relating transformers to models and neural representations of the hippocampal formation | https://openreview.net/forum?id=B8DVo9B1YE0 | https://openreview.net/forum?id=B8DVo9B1YE0 | James C. R. Whittington,Joseph Warren,Tim E.J. Behrens | ICLR 2022,Poster | Many deep neural network architectures loosely based on brain networks have recently been shown to replicate neural firing patterns observed in the brain. One of the most exciting and promising novel architectures, the Transformer neural network, was developed without the brain in mind. In this work, we show that transformers, when equipped with recurrent position encodings, replicate the precisely tuned spatial representations of the hippocampal formation; most notably place and grid cells. Furthermore, we show that this result is no surprise since it is closely related to current hippocampal models from neuroscience. We additionally show the transformer version offers dramatic performance gains over the neuroscience version. This work continues to bind computations of artificial and brain networks, offers a novel understanding of the hippocampal-cortical interaction, and suggests how wider cortical areas may perform complex tasks beyond current neuroscience models such as language comprehension. | https://openreview.net/pdf/684a0f0eef15279b5b52184a954e3ca24a2217ac.pdf |
How many degrees of freedom do we need to train deep networks: a loss landscape perspective | https://openreview.net/forum?id=ChMLTGRjFcU | https://openreview.net/forum?id=ChMLTGRjFcU | Brett W Larsen,Stanislav Fort,Nic Becker,Surya Ganguli | ICLR 2022,Poster | A variety of recent works, spanning pruning, lottery tickets, and training within random subspaces, have shown that deep neural networks can be trained using far fewer degrees of freedom than the total number of parameters. We analyze this phenomenon for random subspaces by first examining the success probability of hitting a training loss sublevel set when training within a random subspace of a given training dimensionality. We find a sharp phase transition in the success probability from $0$ to $1$ as the training dimension surpasses a threshold. This threshold training dimension increases as the desired final loss decreases, but decreases as the initial loss decreases. We then theoretically explain the origin of this phase transition, and its dependence on initialization and final desired loss, in terms of properties of the high-dimensional geometry of the loss landscape. In particular, we show via Gordon's escape theorem, that the training dimension plus the Gaussian width of the desired loss sublevel set, projected onto a unit sphere surrounding the initialization, must exceed the total number of parameters for the success probability to be large. In several architectures and datasets, we measure the threshold training dimension as a function of initialization and demonstrate that it is a small fraction of the total parameters, implying by our theory that successful training with so few dimensions is possible precisely because the Gaussian width of low loss sublevel sets is very large. Moreover, we compare this threshold training dimension to more sophisticated ways of reducing training degrees of freedom, including lottery tickets as well as a new, analogous method: lottery subspaces. | https://openreview.net/pdf/0801cd7dac7a5c8a0c9d6202ed146b1662b1ccdc.pdf |
Is Importance Weighting Incompatible with Interpolating Classifiers? | https://openreview.net/forum?id=uqBOne3LUKy | https://openreview.net/forum?id=uqBOne3LUKy | Ke Alexander Wang,Niladri Shekhar Chatterji,Saminul Haque,Tatsunori Hashimoto | ICLR 2022,Poster | Importance weighting is a classic technique to handle distribution shifts. However, prior work has presented strong empirical and theoretical evidence demonstrating that importance weights can have little to no effect on overparameterized neural networks. \emph{Is importance weighting truly incompatible with the training of overparameterized neural networks?} Our paper answers this in the negative. We show that importance weighting fails not because of the overparameterization, but instead, as a result of using exponentially-tailed losses like the logistic or cross-entropy loss. As a remedy, we show that polynomially-tailed losses restore the effects of importance reweighting in correcting distribution shift in overparameterized models. We characterize the behavior of gradient descent on importance weighted polynomially-tailed losses with overparameterized linear models, and theoretically demonstrate the advantage of using polynomially-tailed losses in a label shift setting. Surprisingly, our theory shows that using weights that are obtained by exponentiating the classical unbiased importance weights can improve performance. Finally, we demonstrate the practical value of our analysis with neural network experiments on a subpopulation shift and a label shift dataset. When reweighted, our loss function can outperform reweighted cross-entropy by as much as 9\% in test accuracy. Our loss function also gives test accuracies comparable to, or even exceeding, well-tuned state-of-the-art methods for correcting distribution shifts. | https://openreview.net/pdf/a1dcd84e1b3a313fb6b6f62958fef613ebefa9d5.pdf |
Neural Models for Output-Space Invariance in Combinatorial Problems | https://openreview.net/forum?id=ibrUkC-pbis | https://openreview.net/forum?id=ibrUkC-pbis | Yatin Nandwani,Vidit Jain,Mausam .,Parag Singla | ICLR 2022,Poster | Recently many neural models have been proposed to solve combinatorial puzzles by implicitly learning underlying constraints using their solved instances, such as sudoku or graph coloring (GCP). One drawback of the proposed architectures, which are often based on Graph Neural Networks (GNN) (Zhou et al., 2020), is that they cannot generalize across the size of the output space from which variables are assigned a value, for example, set of colors in a GCP, or board-size in sudoku. We call the output space for the variables as ‘value-set’. While many works have demonstrated generalization of GNNs across graph size, there has been no study on how to design a GNN for achieving value-set invariance for problems that come from the same domain. For example, learning to solve 16 x 16 sudoku after being trained on only 9 x 9 sudokus, or coloring a 7 colorable graph after training on 4 colorable graphs. In this work, we propose novel methods to extend GNN based architectures to achieve value-set invariance. Specifically, our model builds on recently proposed Recurrent Relational Networks (RRN) (Palm et al., 2018). Our first approach exploits the graph-size invariance of GNNs by converting a multi-class node classification problem into a binary node classification problem. Our second approach works directly with multiple classes by adding multiple nodes corresponding to the values in the value-set, and then connecting variable nodes to value nodes depending on the problem initialization. Our experimental evaluation on three different combinatorial problems demonstrates that both our models perform well on our novel problem, compared to a generic neural reasoner. Between two of our models, we observe an inherent trade-off: while the binarized model gives better performance when trained on smaller value-sets, multi-valued model is much more memory efficient, resulting in improved performance when trained on larger value-sets, where binarized model fails to train. | https://openreview.net/pdf/372d1714883eb775a0b6579118d157fcb97f83bc.pdf |
StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis | https://openreview.net/forum?id=iUuzzTMUw9K | https://openreview.net/forum?id=iUuzzTMUw9K | Jiatao Gu,Lingjie Liu,Peng Wang,Christian Theobalt | ICLR 2022,Poster | We propose StyleNeRF, a 3D-aware generative model for photo-realistic high-resolution image synthesis with high multi-view consistency, which can be trained on unstructured 2D images. Existing approaches either cannot synthesize high-resolution images with fine details or yield clearly noticeable 3D-inconsistent artifacts. In addition, many of them lack control on style attributes and explicit 3D camera poses. To address these issues, StyleNeRF integrates the neural radiance field (NeRF) into a style-based generator to tackle the aforementioned challenges, i.e., improving rendering efficiency and 3D consistency for high-resolution image generation. To address the first issue, we perform volume rendering only to produce a low-resolution feature map, and progressively apply upsampling in 2D. To mitigate the inconsistencies caused by 2D upsampling, we propose multiple designs including a better upsampler choice and a new regularization loss to enforce 3D consistency. With these designs, StyleNeRF is able to synthesize high-resolution images at interactive rates while preserving 3D consistency at high quality. StyleNeRF also enables control of camera poses and different levels of styles, which can generalize to unseen views. It also supports challenging tasks such as style mixing, inversion and simple semantic edits.
| https://openreview.net/pdf/ac9a5093dd4cf1624afa5fa1f5fea74d822061c6.pdf |
The Role of Pretrained Representations for the OOD Generalization of RL Agents | https://openreview.net/forum?id=8eb12UQYxrG | https://openreview.net/forum?id=8eb12UQYxrG | Frederik Träuble,Andrea Dittadi,Manuel Wuthrich,Felix Widmaier,Peter Vincent Gehler,Ole Winther,Francesco Locatello,Olivier Bachem,Bernhard Schölkopf,Stefan Bauer | ICLR 2022,Poster | Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facilitate efficient downstream learning and generalization. By training 240 representations and over 10,000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of pretrained VAE-based representations affect the OOD generalization of downstream agents. We observe that many agents are surprisingly robust to realistic distribution shifts, including the challenging sim-to-real case. In addition, we find that the generalization performance of a simple downstream proxy task reliably predicts the generalization performance of our RL agents under a wide range of OOD settings. Such proxy tasks can thus be used to select pretrained representations that will lead to agents that generalize. | https://openreview.net/pdf/21a137cbbf57e242ed59de220b1d44db251017d9.pdf |
Enabling Arbitrary Translation Objectives with Adaptive Tree Search | https://openreview.net/forum?id=rhOiUS8KQM9 | https://openreview.net/forum?id=rhOiUS8KQM9 | Wang Ling,Wojciech Stokowiec,Domenic Donato,Chris Dyer,Lei Yu,Laurent Sartran,Austin Matthews | ICLR 2022,Poster | We introduce an adaptive tree search algorithm, which is a deterministic variant of Monte Carlo tree search, that can find high-scoring outputs under translation models that make no assumptions about the form or structure of the search objective. This algorithm enables the exploration of new kinds of models that are unencumbered by constraints imposed to make decoding tractable, such as autoregressivity or conditional independence assumptions. When applied to autoregressive models, our algorithm has different biases than beam search has, which enables a new analysis of the role of decoding bias in autoregressive models. Empirically, we show that our adaptive tree search algorithm finds outputs with substantially better model scores compared to beam search in autoregressive models, and compared to reranking techniques in models whose scores do not decompose additively with respect to the words in the output. We also characterise the correlation of several translation model objectives with respect to BLEU. We find that while some standard models are poorly calibrated and benefit from the beam search bias, other often more robust models (autoregressive models tuned to maximize expected automatic metric scores, the noisy channel model and a newly proposed objective) benefit from increasing amounts of search using our proposed decoder, whereas the beam search bias limits the improvements obtained from such objectives. Thus, we argue that as models improve, the improvements may be masked by over-reliance on beam search or reranking based methods. | https://openreview.net/pdf/db1b6e5bc8b72c86683c9aae725f36f188279740.pdf |
Proof Artifact Co-Training for Theorem Proving with Language Models | https://openreview.net/forum?id=rpxJc9j04U | https://openreview.net/forum?id=rpxJc9j04U | Jesse Michael Han,Jason Rute,Yuhuai Wu,Edward Ayers,Stanislas Polu | ICLR 2022,Poster | Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce as such libraries require years of concentrated effort by human specialists to be built. This is particularly challenging when applying large Transformer language models to tactic prediction, because the scaling of performance with respect to model size is quickly disrupted in the data-scarce, easily-overfitted regime. We propose PACT (Proof Artifact Co-Training), a general methodology for extracting abundant self-supervised data from kernel-level proof terms for joint training alongside the usual tactic prediction objective. We apply this methodology to Lean,an interactive proof assistant which hosts some of the most sophisticated formalized mathematics to date. We instrument Lean with a neural theorem prover driven by a Transformer language model and show that PACT improves theorem proving success rate on a held-out suite of test theorems from 32% to 48%. | https://openreview.net/pdf/452a6f034546f7c5332394391a04b08aafdba0f7.pdf |
Mirror Descent Policy Optimization | https://openreview.net/forum?id=aBO5SvgSt1 | https://openreview.net/forum?id=aBO5SvgSt1 | Manan Tomar,Lior Shani,Yonathan Efroni,Mohammad Ghavamzadeh | ICLR 2022,Poster | Mirror descent (MD), a well-known first-order method in constrained convex optimization, has recently been shown as an important tool to analyze trust-region algorithms in reinforcement learning (RL). However, there remains a considerable gap between such theoretically analyzed algorithms and the ones used in practice. Inspired by this, we propose an efficient RL algorithm, called {\em mirror descent policy optimization} (MDPO). MDPO iteratively updates the policy by {\em approximately} solving a trust-region problem, whose objective function consists of two terms: a linearization of the standard RL objective and a proximity term that restricts two consecutive policies to be close to each other. Each update performs this approximation by taking multiple gradient steps on this objective function. We derive {\em on-policy} and {\em off-policy} variants of MDPO, while emphasizing important design choices motivated by the existing theory of MD in RL. We highlight the connections between on-policy MDPO and two popular trust-region RL algorithms: TRPO and PPO, and show that explicitly enforcing the trust-region constraint is in fact {\em not} a necessity for high performance gains in TRPO. We then show how the popular soft actor-critic (SAC) algorithm can be derived by slight modifications of off-policy MDPO. Overall, MDPO is derived from the MD principles, offers a unified approach to viewing a number of popular RL algorithms, and performs better than or on-par with TRPO, PPO, and SAC in a number of continuous and discrete control tasks. | https://openreview.net/pdf/03dfa6176ca266c8fa73ec81fc9a35d808fa7ad3.pdf |
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models | https://openreview.net/forum?id=OcKMT-36vUs | https://openreview.net/forum?id=OcKMT-36vUs | Justin Gilmer,Behrooz Ghorbani,Ankush Garg,Sneha Kudugunta,Behnam Neyshabur,David Cardoze,George Edward Dahl,Zachary Nado,Orhan Firat | ICLR 2022,Poster | In this work, we study the evolution of the loss Hessian across many classification tasks in order to understand the effect the curvature of the loss has on the training dynamics. Whereas prior work has focused on how different learning rates affect the loss Hessian observed during training, we also analyze the effects of model initialization, architectural choices, and common training heuristics such as gradient clipping and learning rate warmup. Our results demonstrate that successful model and hyperparameter choices allow the early optimization trajectory to either avoid---or navigate out of---regions of high curvature and into flatter regions that tolerate a higher learning rate. Our results suggest a unifying perspective on how disparate mitigation strategies for training instability ultimately address the same underlying failure mode of neural network optimization, namely poor conditioning. Inspired by the conditioning perspective, we show that learning rate warmup can improve training stability just as much as batch normalization, layer normalization, MetaInit, GradInit, and Fixup initialization. | https://openreview.net/pdf/0e01902abf6d0c88e31b87ec9ca863b66ac6e85c.pdf |
Cross-Domain Imitation Learning via Optimal Transport | https://openreview.net/forum?id=xP3cPq2hQC | https://openreview.net/forum?id=xP3cPq2hQC | Arnaud Fickinger,Samuel Cohen,Stuart Russell,Brandon Amos | ICLR 2022,Poster | Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology. Comparing trajectories and stationary distributions between the expert and imitation agents is challenging because they live on different systems that may not even have the same dimensionality. We propose Gromov-Wasserstein Imitation Learning (GWIL), a method for cross-domain imitation that uses the Gromov-Wasserstein distance to align and compare states between the different spaces of the agents. Our theory formally characterizes the scenarios where GWIL preserves optimality, revealing its possibilities and limitations. We demonstrate the effectiveness of GWIL in non-trivial continuous control domains ranging from simple rigid transformation of the expert domain to arbitrary transformation of the state-action space. | https://openreview.net/pdf/0e1aa9f9ddcbcd903dbecbe6f034779d158d2260.pdf |
Large-Scale Representation Learning on Graphs via Bootstrapping | https://openreview.net/forum?id=0UXT6PpRpW | https://openreview.net/forum?id=0UXT6PpRpW | Shantanu Thakoor,Corentin Tallec,Mohammad Gheshlaghi Azar,Mehdi Azabou,Eva L Dyer,Remi Munos,Petar Veličković,Michal Valko | ICLR 2022,Poster | Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs. However, to achieve state-of-the-art performance, methods often need large numbers of negative examples and rely on complex augmentations. This can be prohibitively expensive, especially for large graphs. To address these challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph representation learning method that learns by predicting alternative augmentations of the input. BGRL uses only simple augmentations and alleviates the need for contrasting with negative examples, and thus is scalable by design. BGRL outperforms or matches prior methods on several established benchmarks, while achieving a 2-10x reduction in memory costs. Furthermore, we show that BGRL can be scaled up to extremely large graphs with hundreds of millions of nodes in the semi-supervised regime, achieving state-of-the-art performance and improving over supervised baselines where representations are shaped only through label information. In particular, our solution centered on BGRL constituted one of the winning entries to the Open Graph Benchmark -Large Scale Challenge at KDD Cup 2021, on a graph orders of magnitudes larger than all previously available benchmarks, thus demonstrating the scalability and effectiveness of our approach. | https://openreview.net/pdf/5e487c083a8ccf77a044bed028e5f4bc7d61c314.pdf |
Robust and Scalable SDE Learning: A Functional Perspective | https://openreview.net/forum?id=xZ6H7wydGl | https://openreview.net/forum?id=xZ6H7wydGl | Scott Alexander Cameron,Tyron Luke Cameron,Arnu Pretorius,Stephen J. Roberts | ICLR 2022,Poster | Stochastic differential equations provide a rich class of flexible generative
models, capable of describing a wide range of spatio-temporal processes. A host
of recent work looks to learn data-representing SDEs, using neural networks and
other flexible function approximators. Despite these advances, learning remains
computationally expensive due to the sequential nature of SDE integrators. In
this work, we propose an importance-sampling estimator for probabilities of
observations of SDEs for the purposes of learning. Crucially, the approach we
suggest does not rely on such integrators. The proposed method produces
lower-variance gradient estimates compared to algorithms based on SDE
integrators and has the added advantage of being embarrassingly parallelizable.
This facilitates the effective use of large-scale parallel hardware for massive
decreases in computation time.
| https://openreview.net/pdf/59f33538897ccebf6c85feda2739b570f0445ebd.pdf |
Neural Processes with Stochastic Attention: Paying more attention to the context dataset | https://openreview.net/forum?id=JPkQwEdYn8 | https://openreview.net/forum?id=JPkQwEdYn8 | Mingyu Kim,Kyeong Ryeol Go,Se-Young Yun | ICLR 2022,Poster | Neural processes (NPs) aim to stochastically complete unseen data points based on a given context dataset. NPs essentially leverage a given dataset as a context representation to derive a suitable identifier for a novel task. To improve the prediction accuracy, many variants of NPs have investigated context embedding approaches that generally design novel network architectures and aggregation functions satisfying permutation invariant. In this work, we propose a stochastic attention mechanism for NPs to capture appropriate context information. From the perspective of information theory, we demonstrate that the proposed method encourages context embedding to be differentiated from a target dataset, allowing NPs to consider features in a target dataset and context embedding independently. We observe that the proposed method can appropriately capture context embedding even under noisy data sets and restricted task distributions, where typical NPs suffer from a lack of context embeddings. We empirically show that our approach substantially outperforms conventional NPs in various domains through 1D regression, predator-prey model, and image completion. Moreover, the proposed method is also validated by MovieLens-10k dataset, a real-world problem. | https://openreview.net/pdf/f1bb535788b750cfd30f0aa321b7bcfa2a50924b.pdf |
Evaluating Disentanglement of Structured Representations | https://openreview.net/forum?id=SLz5sZjacp | https://openreview.net/forum?id=SLz5sZjacp | Raphaël Dang-Nhu | ICLR 2022,Poster | We introduce the first metric for evaluating disentanglement at individual hierarchy levels of a structured latent representation. Applied to object-centric generative models, this offers a systematic, unified approach to evaluating (i) object separation between latent slots (ii) disentanglement of object properties inside individual slots (iii) disentanglement of intrinsic and extrinsic object properties. We theoretically show that our framework gives stronger guarantees of selecting a good model than previous disentanglement metrics. Experimentally, we demonstrate that viewing object compositionality as a disentanglement problem addresses several issues with prior visual metrics of object separation. As a core technical component, we present the first representation probing algorithm handling slot permutation invariance. | https://openreview.net/pdf/c64b7f2fadb664b509c8b2e80def8ea967bb95a3.pdf |
Geometric Transformers for Protein Interface Contact Prediction | https://openreview.net/forum?id=CS4463zx6Hi | https://openreview.net/forum?id=CS4463zx6Hi | Alex Morehead,Chen Chen,Jianlin Cheng | ICLR 2022,Poster | Computational methods for predicting the interface contacts between proteins come highly sought after for drug discovery as they can significantly advance the accuracy of alternative approaches, such as protein-protein docking, protein function analysis tools, and other computational methods for protein bioinformatics. In this work, we present the Geometric Transformer, a novel geometry-evolving graph transformer for rotation and translation-invariant protein interface contact prediction, packaged within DeepInteract, an end-to-end prediction pipeline. DeepInteract predicts partner-specific protein interface contacts (i.e., inter-protein residue-residue contacts) given the 3D tertiary structures of two proteins as input. In rigorous benchmarks, DeepInteract, on challenging protein complex targets from the 13th and 14th CASP-CAPRI experiments as well as Docking Benchmark 5, achieves 14% and 1.1% top L/5 precision (L: length of a protein unit in a complex), respectively. In doing so, DeepInteract, with the Geometric Transformer as its graph-based backbone, outperforms existing methods for interface contact prediction in addition to other graph-based neural network backbones compatible with DeepInteract, thereby validating the effectiveness of the Geometric Transformer for learning rich relational-geometric features for downstream tasks on 3D protein structures. | https://openreview.net/pdf/539393e93bb75c9c05221839904c4d0b83d67adf.pdf |
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions | https://openreview.net/forum?id=E4EE_ohFGz | https://openreview.net/forum?id=E4EE_ohFGz | Chen Zhu,Zheng Xu,Mingqing Chen,Jakub Konečný,Andrew Hard,Tom Goldstein | ICLR 2022,Poster | Federated learning has been deployed to train machine learning models from decentralized client data on mobile devices in practice. The clients available for training are observed to have periodically shifting distributions changing with the time of day, which can cause instability in training and degrade the model performance. In this paper, instead of modeling the distribution shift with a block-cyclic pattern as previous works, we model it with a mixture of distributions that gradually shifts between daytime and nighttime modes, and find this intuitive model to better match the observations in practical federated learning systems.
Furthermore, we propose to jointly train a clustering model and a multi-branch network to allocate lightweight specialized branches to clients from different modes. A temporal prior is used to significantly boost the training performance.
Experiments for image classification on EMNIST and CIFAR datasets, and next word prediction on the Stack Overflow dataset show that the proposed algorithm can counter the effects of the distribution shift and significantly improve the final model performance. | https://openreview.net/pdf/9b5ecb00ea582bf0bc094c59b0c48d7c18be9def.pdf |
IGLU: Efficient GCN Training via Lazy Updates | https://openreview.net/forum?id=5kq11Tl1z4 | https://openreview.net/forum?id=5kq11Tl1z4 | S Deepak Narayanan,Aditya Sinha,Prateek Jain,Purushottam Kar,SUNDARARAJAN SELLAMANICKAM | ICLR 2022,Poster | Training multi-layer Graph Convolution Networks (GCN) using standard SGD techniques scales poorly as each descent step ends up updating node embeddings for a large portion of the graph. Recent attempts to remedy this sub-sample the graph that reduces compute but introduce additional variance and may offer suboptimal performance. This paper develops the IGLU method that caches intermediate computations at various GCN layers thus enabling lazy updates that significantly reduce the compute cost of descent. IGLU introduces bounded bias into the gradients but nevertheless converges to a first-order saddle point under standard assumptions such as objective smoothness. Benchmark experiments show that IGLU offers up to 1.2% better accuracy despite requiring up to 88% less compute. | https://openreview.net/pdf/c65e33e9b21b066dc663b67740ec61e057d414c8.pdf |
Procedural generalization by planning with self-supervised world models | https://openreview.net/forum?id=FmBegXJToY | https://openreview.net/forum?id=FmBegXJToY | Ankesh Anand,Jacob C Walker,Yazhe Li,Eszter Vértes,Julian Schrittwieser,Sherjil Ozair,Theophane Weber,Jessica B Hamrick | ICLR 2022,Poster | One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of model-based agents is not well understood because existing work has focused on model-free agents when benchmarking generalization. Here, we explicitly measure the generalization ability of model-based agents in comparison to their model-free counterparts. We focus our analysis on MuZero (Schrittwieser et al., 2020), a powerful model-based agent, and evaluate its performance on both procedural and task generalization. We identify three factors of procedural generalization---planning, self-supervised representation learning, and procedural data diversity---and show that by combining these techniques, we achieve state-of-the art generalization performance and data efficiency on Procgen (Cobbe et al., 2019). However, we find that these factors do not always provide the same benefits for the task generalization benchmarks in Meta-World (Yu et al., 2019), indicating that transfer remains a challenge and may require different approaches than procedural generalization. Overall, we suggest that building generalizable agents requires moving beyond the single-task, model-free paradigm and towards self-supervised model-based agents that are trained in rich, procedural, multi-task environments. | https://openreview.net/pdf/aff2220dd1aea0f7b326e525fa1ed3f25efe2eb8.pdf |
Top-N: Equivariant Set and Graph Generation without Exchangeability | https://openreview.net/forum?id=-Gk_IPJWvk | https://openreview.net/forum?id=-Gk_IPJWvk | Clement Vignac,Pascal Frossard | ICLR 2022,Poster | This work addresses one-shot set and graph generation, and, more specifically, the parametrization of probabilistic decoders that map a vector-shaped prior to a distribution over sets or graphs. Sets and graphs are most commonly generated by first sampling points i.i.d. from a normal distribution, and then processing these points along with the prior vector using Transformer layers or Graph Neural Networks.
This architecture is designed to generate exchangeable distributions, i.e., all permutations of the generated outputs are equally likely. We however show that it only optimizes a proxy to the evidence lower bound, which makes it hard to train. We then study equivariance in generative settings and show that non-exchangeable methods can still achieve permutation equivariance. Using this result, we introduce Top-n creation, a differentiable generation mechanism that uses the latent vector to select the most relevant points from a trainable reference set. Top-n can replace i.i.d. generation in any Variational Autoencoder or Generative Adversarial Network. Experimentally, our method outperforms i.i.d. generation by 15% at SetMNIST reconstruction, by 33% at object detection on CLEVR, generates sets that are 74% closer to the true distribution on a synthetic molecule-like dataset, and generates more valid molecules on QM9. | https://openreview.net/pdf/8dd83e84eec6c2706847d33a5777775604829ad9.pdf |
The Spectral Bias of Polynomial Neural Networks | https://openreview.net/forum?id=P7FLfMLTSEX | https://openreview.net/forum?id=P7FLfMLTSEX | Moulik Choraria,Leello Tadesse Dadi,Grigorios Chrysos,Julien Mairal,Volkan Cevher | ICLR 2022,Poster | Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a $\text{\it{spectral bias}}$ towards low-frequency functions, which yields faster learning of low-frequency components during training. Inspired by such studies, we conduct a spectral analysis of the Neural Tangent Kernel (NTK) of PNNs. We find that the $\Pi$-Net family, i.e., a recently proposed parametrization of PNNs, speeds up the learning of the higher frequencies.
We verify the theoretical bias through extensive experiments. We expect our analysis to provide novel insights into designing architectures and learning frameworks by incorporating multiplicative interactions via polynomials.
| https://openreview.net/pdf/e0444281519be29bf9b0bf62909377028d4cb017.pdf |
Invariant Causal Representation Learning for Out-of-Distribution Generalization | https://openreview.net/forum?id=-e4EXDWXnSn | https://openreview.net/forum?id=-e4EXDWXnSn | Chaochao Lu,Yuhuai Wu,José Miguel Hernández-Lobato,Bernhard Schölkopf | ICLR 2022,Poster | Due to spurious correlations, machine learning systems often fail to generalize to environments whose distributions differ from the ones used at training time. Prior work addressing this, either explicitly or implicitly, attempted to find a data representation that has an invariant relationship with the target. This is done by leveraging a diverse set of training environments to reduce the effect of spurious features and build an invariant predictor. However, these methods have generalization guarantees only when both data representation and classifiers come from a linear model class. We propose invariant Causal Representation Learning (iCaRL), an approach that enables out-of-distribution (OOD) generalization in the nonlinear setting (i.e., nonlinear representations and nonlinear classifiers). It builds upon a practical and general assumption: the prior over the data representation (i.e., a set of latent variables encoding the data) given the target and the environment belongs to general exponential family distributions, i.e., a more flexible conditionally non-factorized prior that can actually capture complicated dependences between the latent variables. Based on this, we show that it is possible to identify the data representation up to simple transformations. We also show that all direct causes of the target can be fully discovered, which further enables us to obtain generalization guarantees in the nonlinear setting. Experiments on both synthetic and real-world datasets demonstrate that our approach outperforms a variety of baseline methods. | https://openreview.net/pdf/131aed68985b1886dbcb75f29a9399e1da984948.pdf |
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 | https://openreview.net/forum?id=HCRVf71PMF | https://openreview.net/forum?id=HCRVf71PMF | Chengwei Qin,Shafiq Joty | ICLR 2022,Poster | Existing approaches to lifelong language learning rely on plenty of labeled data for learning a new task, which is hard to obtain in most real scenarios. Considering that humans can continually learn new tasks from a handful of examples, we expect the models also to be able to generalize well on new few-shot tasks without forgetting the previous ones. In this work, we define this more challenging yet practical problem as Lifelong Few-shot Language Learning (LFLL) and propose a unified framework for it based on prompt tuning of T5. Our framework called LFPT5 takes full advantage of PT's strong few-shot learning ability, and simultaneously trains the model as a task solver and a data generator. Before learning a new domain of the same task type, LFPT5 generates pseudo (labeled) samples of previously learned domains, and later gets trained on those samples to alleviate forgetting of previous knowledge as it learns the new domain. In addition, a KL divergence loss is minimized to achieve label consistency between the previous and the current model. While adapting to a new task type, LFPT5 includes and tunes additional prompt embeddings for the new task. With extensive experiments, we demonstrate that LFPT5 can be applied to various different types of tasks and significantly outperform previous methods in different LFLL settings. | https://openreview.net/pdf/a61cfe769dd99962e037c723c20da960f1378949.pdf |
On Non-Random Missing Labels in Semi-Supervised Learning | https://openreview.net/forum?id=6yVvwR9H9Oj | https://openreview.net/forum?id=6yVvwR9H9Oj | Xinting Hu,Yulei Niu,Chunyan Miao,Xian-Sheng Hua,Hanwang Zhang | ICLR 2022,Poster | Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in which the label Missing Not At Random (MNAR) problem is more realistic and challenging, compared to the widely-adopted yet naive Missing Completely At Random assumption where both labeled and unlabeled data share the same class distribution. Different from existing SSL solutions that overlook the role of ''class'' in causing the non-randomness, e.g., users are more likely to label popular classes, we explicitly incorporate ''class'' into SSL. Our method is three-fold: 1) We propose Class-Aware Propensity (CAP) that exploits the unlabeled data to train an improved classifier using the biased labeled data. 2) To encourage rare class training, whose model is low-recall but high-precision that discards too many pseudo-labeled data, we propose Class-Aware Imputation (CAI) that dynamically decreases (or increases) the pseudo-label assignment threshold for rare (or frequent) classes. 3) Overall, we integrate CAP and CAI into a Class-Aware Doubly Robust (CADR) estimator for training an unbiased SSL model. Under various MNAR settings and ablations, our method not only significantly outperforms existing baselines, but also surpasses other label bias removal SSL methods.
| https://openreview.net/pdf/5b4b2c13eef5b39bb9f1edf005023f268f76c88d.pdf |
Mapping conditional distributions for domain adaptation under generalized target shift | https://openreview.net/forum?id=sPfB2PI87BZ | https://openreview.net/forum?id=sPfB2PI87BZ | Matthieu Kirchmeyer,Alain Rakotomamonjy,Emmanuel de Bezenac,patrick gallinari | ICLR 2022,Poster | We consider the problem of unsupervised domain adaptation (UDA) between a source and a target domain under conditional and label shift a.k.a Generalized Target Shift (GeTarS). Unlike simpler UDA settings, few works have addressed this challenging problem. Recent approaches learn domain-invariant representations, yet they have practical limitations and rely on strong assumptions that may not hold in practice. In this paper, we explore a novel and general approach to align pretrained representations, which circumvents existing drawbacks. Instead of constraining representation invariance, it learns an optimal transport map, implemented as a NN, which maps source representations onto target ones. Our approach is flexible and scalable, it preserves the problem's structure and it has strong theoretical guarantees under mild assumptions. In particular, our solution is unique, matches conditional distributions across domains, recovers target proportions and explicitly controls the target generalization risk. Through an exhaustive comparison on several datasets, we challenge the state-of-the-art in GeTarS. | https://openreview.net/pdf/57e7b71f45b2a39eb5f81670fb7e8fd080744a11.pdf |
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications | https://openreview.net/forum?id=oWZsQ8o5EA | https://openreview.net/forum?id=oWZsQ8o5EA | Ziqiao Wang,Yongyi Mao | ICLR 2022,Poster | This paper follows up on a recent work of Neu et al. (2021) and presents some new information-theoretic upper bounds for the generalization error of machine learning models, such as neural networks, trained with SGD. We apply these bounds to analyzing the generalization behaviour of linear and two-layer ReLU networks. Experimental study of these bounds provide some insights on the SGD training of neural networks. They also point to a new and simple regularization scheme which we show performs comparably to the current state of the art. | https://openreview.net/pdf/2de81438aaadab6bf2476214305bff6725392d71.pdf |
Amortized Implicit Differentiation for Stochastic Bilevel Optimization | https://openreview.net/forum?id=3PN4iyXBeF | https://openreview.net/forum?id=3PN4iyXBeF | Michael Arbel,Julien Mairal | ICLR 2022,Poster | We study a class of algorithms for solving bilevel optimization problems in both stochastic and deterministic settings when the inner-level objective is strongly convex. Specifically, we consider algorithms based on inexact implicit differentiation and we exploit a warm-start strategy to amortize the estimation of the exact gradient. We then introduce a unified theoretical framework inspired by the study of singularly perturbed systems to analyze such amortized algorithms. By using this framework, our analysis shows these algorithms to match the computational complexity of oracle methods that have access to an unbiased estimate of the gradient, thus outperforming many existing results for bilevel optimization.
We illustrate these findings on synthetic experiments and demonstrate the efficiency of these algorithms on hyper-parameter optimization experiments involving several thousands of variables. | https://openreview.net/pdf/53d397803e076505ad052eb9a7267b6d0805c0dc.pdf |
Multi-objective Optimization by Learning Space Partition | https://openreview.net/forum?id=FlwzVjfMryn | https://openreview.net/forum?id=FlwzVjfMryn | Yiyang Zhao,Linnan Wang,Kevin Yang,Tianjun Zhang,Tian Guo,Yuandong Tian | ICLR 2022,Poster | In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are not dominated by other feasible solutions. In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier. The partitioning is based on the dominance number, which measures "how close'' a data point is to the Pareto frontier among existing samples. To account for possible partition errors due to limited samples and model mismatch, we leverage Monte Carlo Tree Search (MCTS) to exploit promising regions while exploring suboptimal regions that may turn out to contain good solutions later. Theoretically, we prove the efficacy of learning space partitioning via LaMOO under certain assumptions. Empirically, on the HyperVolume (HV) benchmark, a popular MOO metric, LaMOO substantially outperforms strong baselines on multiple real-world MOO tasks, by up to 225% in sample efficiency for neural architecture search on Nasbench201, and up to 10% for molecular design. | https://openreview.net/pdf/9de2fff362decf573ae08e0c8975856a05f47a3d.pdf |
Mapping Language Models to Grounded Conceptual Spaces | https://openreview.net/forum?id=gJcEM8sxHK | https://openreview.net/forum?id=gJcEM8sxHK | Roma Patel,Ellie Pavlick | ICLR 2022,Poster | A fundamental criticism of text-only language models (LMs) is their lack of grounding---that is, the ability to tie a word for which they have learned a representation, to its actual use in the world. However, despite this limitation, large pre-trained LMs have been shown to have a remarkable grasp of the conceptual structure of language, as demonstrated by their ability to answer questions, generate fluent text, or make inferences about entities, objects, and properties that they have never physically observed. In this work we investigate the extent to which the rich conceptual structure that LMs learn indeed reflects the conceptual structure of the non-linguistic world---which is something that LMs have never observed. We do this by testing whether the LMs can learn to map an entire conceptual domain (e.g., direction or colour) onto a grounded world representation given only a small number of examples. For example, we show a model what the word ``left" means using a textual depiction of a grid world, and assess how well it can generalise to related concepts, for example, the word ``right", in a similar grid world. We investigate a range of generative language models of varying sizes (including GPT-2 and GPT-3), and see that although the smaller models struggle to perform this mapping, the largest model can not only learn to ground the concepts that it is explicitly taught, but appears to generalise to several instances of unseen concepts as well. Our results suggest an alternative means of building grounded language models: rather than learning grounded representations ``from scratch'', it is possible that large text-only models learn a sufficiently rich conceptual structure that could allow them to be grounded in a data-efficient way. | https://openreview.net/pdf/e21987f87f4a8c8ec30cd2613232d4f27014f121.pdf |
The Efficiency Misnomer | https://openreview.net/forum?id=iulEMLYh1uR | https://openreview.net/forum?id=iulEMLYh1uR | Mostafa Dehghani,Yi Tay,Anurag Arnab,Lucas Beyer,Ashish Vaswani | ICLR 2022,Poster | Model efficiency is a critical aspect of developing and deploying machine learning models.
Inference time and latency directly affect the user experience, and some applications have hard requirements. In addition to inference costs, model training also have direct financial and environmental impacts.
Although there are numerous well-established metrics (cost indicators) for measuring model efficiency, researchers and practitioners often assume that these metrics are correlated with each other and report only a few of them.
In this paper, we thoroughly discuss common cost indicators, their advantages and disadvantages, and how they can contradict each other.
We demonstrate how incomplete reporting of cost indicators can lead to partial conclusions and a blurred or incomplete picture of the practical considerations of different models. We further present suggestions to improve reporting of efficiency metrics. | https://openreview.net/pdf/ccde7c84b026b78d9ecf3609e60b0ea1e279d302.pdf |
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface | https://openreview.net/forum?id=auOPcdAcoy | https://openreview.net/forum?id=auOPcdAcoy | Tuan Anh Le,Katherine M. Collins,Luke Hewitt,Kevin Ellis,Siddharth N,Samuel Gershman,Joshua B. Tenenbaum | ICLR 2022,Poster | Modeling complex phenomena typically involves the use of both discrete and continuous variables. Such a setting applies across a wide range of problems, from identifying trends in time-series data to performing effective compositional scene understanding in images. Here, we propose Hybrid Memoised Wake-Sleep (HMWS), an algorithm for effective inference in such hybrid discrete-continuous models. Prior approaches to learning suffer as they need to perform repeated expensive inner-loop discrete inference. We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization. We evaluate HMWS in the GP-kernel learning and 3D scene understanding domains, and show that it outperforms current state-of-the-art inference methods. | https://openreview.net/pdf/4406e9491128f790573cb34e530a68b400ba3b6a.pdf |
Adversarial Retriever-Ranker for Dense Text Retrieval | https://openreview.net/forum?id=MR7XubKUFB | https://openreview.net/forum?id=MR7XubKUFB | Hang Zhang,Yeyun Gong,Yelong Shen,Jiancheng Lv,Nan Duan,Weizhu Chen | ICLR 2022,Poster | Current dense text retrieval models face two typical challenges. First, it adopts a siamese dual-encoder architecture to encode query and document independently for fast indexing and searching, whereas neglecting the finer-grained term-wise interactions. This results in a sub-optimal recall performance. Second, it highly relies on a negative sampling technique to build up the negative documents in its contrastive loss. To address these challenges, we present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker. The two models are jointly optimized according to a minimax adversarial objective: the retriever learns to retrieve negative documents to cheat the ranker, while the ranker learns to rank a collection of candidates including both the ground-truth and the retrieved ones, as well as providing progressive direct feedback to the dual-encoder retriever. Through this adversarial game, the retriever gradually produces harder negative documents to train a better ranker, whereas the cross-encoder ranker provides progressive feedback to improve retriever. We evaluate AR2 on three benchmarks. Experimental results show that AR2 consistently and significantly outperforms existing dense retriever methods and achieves new state-of-the-art results on all of them. This includes the improvements on Natural Questions R@5 to 77.9%(+2.1%), TriviaQA R@5 to 78.2%(+1.4), and MS-MARCO MRR@10 to 39.5%(+1.3%). We will make our code, models, and data publicly available. | https://openreview.net/pdf/f136ae88d6299eb072ca8a5c70978e10cf5559f4.pdf |
Conditioning Sequence-to-sequence Networks with Learned Activations | https://openreview.net/forum?id=t5s-hd1bqLk | https://openreview.net/forum?id=t5s-hd1bqLk | Alberto Gil Couto Pimentel Ramos,Abhinav Mehrotra,Nicholas Donald Lane,Sourav Bhattacharya | ICLR 2022,Poster | Conditional neural networks play an important role in a number of sequence-to-sequence modeling tasks, including personalized sound enhancement (PSE), speaker dependent automatic speech recognition (ASR), and generative modeling such as text-to-speech synthesis. In conditional neural networks, the output of a model is often influenced by a conditioning vector, in addition to the input. Common approaches of conditioning include input concatenation or modulation with the conditioning vector, which comes at a cost of increased model size. In this work, we introduce a novel approach of neural network conditioning by learning intermediate layer activations based on the conditioning vector. We systematically explore and show that learned activation functions can produce conditional models with comparable or better quality, while decreasing model sizes, thus making them ideal candidates for resource-efficient on-device deployment. As exemplary target use-cases we consider (i) the task of PSE as a pre-processing technique for improving telephony or pre-trained ASR performance under noise, and (ii) personalized ASR in single speaker scenarios. We find that conditioning via activation function learning is an effective modeling strategy, suggesting a broad applicability of the proposed technique across a number of application domains. | https://openreview.net/pdf/61c3a9f19dc0bf8c4ab041dd9340f0c73d2c2a8b.pdf |
LOSSY COMPRESSION WITH DISTRIBUTION SHIFT AS ENTROPY CONSTRAINED OPTIMAL TRANSPORT | https://openreview.net/forum?id=BRFWxcZfAdC | https://openreview.net/forum?id=BRFWxcZfAdC | Huan Liu,George Zhang,Jun Chen,Ashish J Khisti | ICLR 2022,Poster | We study an extension of lossy compression where the reconstruction distribution is different from the source distribution in order to account for distributional shift due to processing. We formulate this as a generalization of optimal transport with an entropy bottleneck to account for the rate constraint due to compression. We provide expressions for the tradeoff between compression rate and the achievable distortion with and without shared common randomness between the encoder and decoder. We study the examples of binary, uniform and Gaussian sources (in an asymptotic setting) in detail and demonstrate that shared randomness can strictly improve the tradeoff. For the case without common randomness and squared-Euclidean distortion, we show that the optimal solution partially decouples into the problem of optimal compression and transport and also characterize the penalty associated with fully decoupling them. We provide experimental results by training deep learning end-to-end compression systems for performing denoising on SVHN and super-resolution on MNIST suggesting consistency with our theoretical results. | https://openreview.net/pdf/78a4014e3770c6c6eea378ae2f81625b2241af8e.pdf |
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations | https://openreview.net/forum?id=gKLAAfiytI | https://openreview.net/forum?id=gKLAAfiytI | Rumen Dangovski,Li Jing,Charlotte Loh,Seungwook Han,Akash Srivastava,Brian Cheung,Pulkit Agrawal,Marin Soljacic | ICLR 2022,Poster | In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge. In fact, the property of invariance is a trivial instance of a broader class called equivariance, which can be intuitively understood as the property that representations transform according to the way the inputs transform. Here, we show that rather than using only invariance, pre-training that encourages non-trivial equivariance to some transformations, while maintaining invariance to other transformations, can be used to improve the semantic quality of representations. Specifically, we extend popular SSL methods to a more general framework which we name Equivariant Self-Supervised Learning (E-SSL). In E-SSL, a simple additional pre-training objective encourages equivariance by predicting the transformations applied to the input. We demonstrate E-SSL’s effectiveness empirically on several popular computer vision benchmarks, e.g. improving SimCLR to 72.5% linear probe accuracy on ImageNet. Furthermore, we demonstrate usefulness of E-SSL for applications beyond computer vision; in particular, we show its utility on regression problems in photonics science. Our code, datasets and pre-trained models are available at https://github.com/rdangovs/essl to aid further research in E-SSL. | https://openreview.net/pdf/4b06d5ed7734b3d8e6ca71f132d90bf8a5b910e6.pdf |
Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching | https://openreview.net/forum?id=25kzAhUB1lz | https://openreview.net/forum?id=25kzAhUB1lz | Pierre-Alexandre Kamienny,Jean Tarbouriech,sylvain lamprier,Alessandro Lazaric,Ludovic Denoyer | ICLR 2022,Poster | Learning meaningful behaviors in the absence of reward is a difficult problem in reinforcement learning. A desirable and challenging unsupervised objective is to learn a set of diverse skills that provide a thorough coverage of the state space while being directed, i.e., reliably reaching distinct regions of the environment. In this paper, we build on the mutual information framework for skill discovery and introduce UPSIDE, which addresses the coverage-directedness trade-off in the following ways: 1) We design policies with a decoupled structure of a directed skill, trained to reach a specific region, followed by a diffusing part that induces a local coverage. 2) We optimize policies by maximizing their number under the constraint that each of them reaches distinct regions of the environment (i.e., they are sufficiently discriminable) and prove that this serves as a lower bound to the original mutual information objective. 3) Finally, we compose the learned directed skills into a growing tree that adaptively covers the environment. We illustrate in several navigation and control environments how the skills learned by UPSIDE solve sparse-reward downstream tasks better than existing baselines. | https://openreview.net/pdf/cc127fb35d524312500353fa6195db8fc4418285.pdf |
Normalization of Language Embeddings for Cross-Lingual Alignment | https://openreview.net/forum?id=Nh7CtbyoqV5 | https://openreview.net/forum?id=Nh7CtbyoqV5 | Prince Osei Aboagye,Yan Zheng,Chin-Chia Michael Yeh,Junpeng Wang,Wei Zhang,Liang Wang,Hao Yang,Jeff Phillips | ICLR 2022,Poster | Learning a good transfer function to map the word vectors from two languages into a shared cross-lingual word vector space plays a crucial role in cross-lingual NLP. It is useful in translation tasks and important in allowing complex models built on a high-resource language like English to be directly applied on an aligned low resource language. While Procrustes and other techniques can align language models with some success, it has recently been identified that structural differences (for instance, due to differing word frequency) create different profiles for various monolingual embedding. When these profiles differ across languages, it correlates with how well languages can align and their performance on cross-lingual downstream tasks. In this work, we develop a very general language embedding normalization procedure, building and subsuming various previous approaches, which removes these structural profiles across languages without destroying their intrinsic meaning. We demonstrate that meaning is retained and alignment is improved on similarity, translation, and cross-language classification tasks. Our proposed normalization clearly outperforms all prior approaches like centering and vector normalization on each task and with each alignment approach. | https://openreview.net/pdf/1cdf6da2d049db967f45c6454ba03f40aa1e3849.pdf |
Boosting the Certified Robustness of L-infinity Distance Nets | https://openreview.net/forum?id=Q76Y7wkiji | https://openreview.net/forum?id=Q76Y7wkiji | Bohang Zhang,Du Jiang,Di He,Liwei Wang | ICLR 2022,Poster | Recently, Zhang et al. (2021) developed a new neural network architecture based on $\ell_\infty$-distance functions, which naturally possesses certified $\ell_\infty$ robustness by its construction. Despite the novel design and theoretical foundation, so far the model only achieved comparable performance to conventional networks. In this paper, we make the following two contributions: $\mathrm{(i)}$ We demonstrate that $\ell_\infty$-distance nets enjoy a fundamental advantage in certified robustness over conventional networks (under typical certification approaches); $\mathrm{(ii)}$ With an improved training process we are able to significantly boost the certified accuracy of $\ell_\infty$-distance nets. Our training approach largely alleviates the optimization problem that arose in the previous training scheme, in particular, the unexpected large Lipschitz constant due to the use of a crucial trick called \textit{$\ell_p$-relaxation}. The core of our training approach is a novel objective function that combines scaled cross-entropy loss and clipped hinge loss with a decaying mixing coefficient. Experiments show that using the proposed training strategy, the certified accuracy of $\ell_\infty$-distance net can be dramatically improved from 33.30% to 40.06% on CIFAR-10 ($\epsilon=8/255$), meanwhile outperforming other approaches in this area by a large margin. Our results clearly demonstrate the effectiveness and potential of $\ell_\infty$-distance net for certified robustness. Codes are available at https://github.com/zbh2047/L_inf-dist-net-v2. | https://openreview.net/pdf/64139e9e640cc111e35419ac12e0f222b55ecd9b.pdf |
Stochastic Training is Not Necessary for Generalization | https://openreview.net/forum?id=ZBESeIUB5k | https://openreview.net/forum?id=ZBESeIUB5k | Jonas Geiping,Micah Goldblum,Phil Pope,Michael Moeller,Tom Goldstein | ICLR 2022,Poster | It is widely believed that the implicit regularization of SGD is fundamental to the impressive generalization behavior we observe in neural networks. In this work, we demonstrate that non-stochastic full-batch training can achieve comparably strong performance to SGD on CIFAR-10 using modern architectures. To this end, we show that the implicit regularization of SGD can be completely replaced with explicit regularization. Our observations indicate that the perceived difficulty of full-batch training may be the result of its optimization properties and the disproportionate time and effort spent by the ML community tuning optimizers and hyperparameters for small-batch training. | https://openreview.net/pdf/63b51516e9311da290bf727bbd4e9376f88c0a48.pdf |
Transfer RL across Observation Feature Spaces via Model-Based Regularization | https://openreview.net/forum?id=7KdAoOsI81C | https://openreview.net/forum?id=7KdAoOsI81C | Yanchao Sun,Ruijie Zheng,Xiyao Wang,Andrew E Cohen,Furong Huang | ICLR 2022,Poster | In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramatic changes over time (e.g. increased number of observable features). However, when the observation space changes, the previous policy will likely fail due to the mismatch of input features, and another policy must be trained from scratch, which is inefficient in terms of computation and sample complexity. Following theoretical insights, we propose a novel algorithm which extracts the latent-space dynamics in the source task, and transfers the dynamics model to the target task to use as a model-based regularizer. Our algorithm works for drastic changes of observation space (e.g. from vector-based observation to image-based observation), without any inter-task mapping or any prior knowledge of the target task. Empirical results show that our algorithm significantly improves the efficiency and stability of learning in the target task. | https://openreview.net/pdf/8b952d72e26efd12a72d49901beab7dc48d28ce9.pdf |
GATSBI: Generative Adversarial Training for Simulation-Based Inference | https://openreview.net/forum?id=kR1hC6j48Tp | https://openreview.net/forum?id=kR1hC6j48Tp | Poornima Ramesh,Jan-Matthis Lueckmann,Jan Boelts,Álvaro Tejero-Cantero,David S. Greenberg,Pedro J. Goncalves,Jakob H. Macke | ICLR 2022,Poster | Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods.
Like SBI algorithms, generative adversarial networks (GANs) do not require explicit likelihoods. We study the relationship between SBI and GANs, and introduce GATSBI, an adversarial approach to SBI. GATSBI reformulates the variational objective in an adversarial setting to learn implicit posterior distributions. Inference with GATSBI is amortised across observations, works in high-dimensional posterior spaces and supports implicit priors. We evaluate GATSBI on two common SBI benchmark problems and on two high-dimensional simulators. On a model for wave propagation on the surface of a shallow water body, we show that GATSBI can return well-calibrated posterior estimates even in high dimensions.
On a model of camera optics, it infers a high-dimensional posterior given an implicit prior, and performs better than a
state-of-the-art SBI approach. We also show how GATSBI can be extended to perform sequential posterior estimation to focus on individual observations.
Overall, GATSBI opens up opportunities for leveraging advances in GANs to perform Bayesian inference on high-dimensional simulation-based models. | https://openreview.net/pdf/7dfeefe58037c9529799e1abf5323514fa066f4e.pdf |
Domain Adversarial Training: A Game Perspective | https://openreview.net/forum?id=AwgtcUAhBq | https://openreview.net/forum?id=AwgtcUAhBq | David Acuna,Marc T Law,Guojun Zhang,Sanja Fidler | ICLR 2022,Poster | The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a game theoretical perspective. Defining optimal solutions in domain-adversarial training as a local Nash equilibrium, we show that gradient descent in domain-adversarial training can violate the asymptotic convergence guarantees of the optimizer, oftentimes hindering the transfer performance. Our analysis leads us to replace gradient descent with high-order ODE solvers (i.e., Runge–Kutta), for which we derive asymptotic convergence guarantees. This family of optimizers is significantly more stable and allows more aggressive learning rates, leading to high performance gains when used as a drop-in replacement over standard optimizers. Our experiments show that in conjunction with state-of-the-art domain-adversarial methods, we achieve up to 3.5% improvement with less than of half training iterations. Our optimizers are easy to implement, free of additional parameters, and can be plugged into any domain-adversarial framework. | https://openreview.net/pdf/e7f03a4c2a754cc0f8a114d7a24b3d00814adfcd.pdf |
Differentiable Expectation-Maximization for Set Representation Learning | https://openreview.net/forum?id=MXdFBmHT4C | https://openreview.net/forum?id=MXdFBmHT4C | Minyoung Kim | ICLR 2022,Poster | We tackle the set2vec problem, the task of extracting a vector representation from an input set comprised of a variable number of feature vectors. Although recent approaches based on self attention such as (Set)Transformers were very successful due to the capability of capturing complex interaction between set elements, the computational overhead is the well-known downside. The inducing-point attention and the latest optimal transport kernel embedding (OTKE) are promising remedies that attain comparable or better performance with reduced computational cost, by incorporating a fixed number of learnable queries in attention. In this paper we approach the set2vec problem from a completely different perspective. The elements of an input set are considered as i.i.d.~samples from a mixture distribution, and we define our set embedding feed-forward network as the maximum-a-posterior (MAP) estimate of the mixture which is approximately attained by a few Expectation-Maximization (EM) steps. The whole MAP-EM steps are differentiable operations with a fixed number of mixture parameters, allowing efficient auto-diff back-propagation for any given downstream task. Furthermore, the proposed mixture set data fitting framework allows unsupervised set representation learning naturally via marginal likelihood maximization aka the empirical Bayes. Interestingly, we also find that OTKE can be seen as a special case of our framework, specifically a single-step EM with extra balanced assignment constraints on the E-step. Compared to OTKE, our approach provides more flexible set embedding as well as prior-induced model regularization. We evaluate our approach on various tasks demonstrating improved performance over the state-of-the-arts. | https://openreview.net/pdf/7042a234575b76e9959702f6b853e880419fa525.pdf |
Overcoming The Spectral Bias of Neural Value Approximation | https://openreview.net/forum?id=vIC-xLFuM6 | https://openreview.net/forum?id=vIC-xLFuM6 | Ge Yang,Anurag Ajay,Pulkit Agrawal | ICLR 2022,Poster | Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest of the algorithm. While multi-layer perceptron networks are universal function approximators, recent works in neural kernel regression suggest the presence of a \textit{spectral bias}, where fitting high-frequency components of the value function requires exponentially more gradient update steps than the low-frequency ones. In this work, we re-examine off-policy reinforcement learning through the lens of kernel regression and propose to overcome such bias via a composite neural tangent kernel. With just a single line-change, our approach, the Fourier feature networks (FFN) produce state-of-the-art performance on challenging continuous control domains with only a fraction of the compute. Faster convergence and better off-policy stability also make it possible to remove the target network without suffering catastrophic divergences, which further reduces TD(0)'s estimation bias on a few tasks. Code and analysis available at https://geyang.github.io/ffn. | https://openreview.net/pdf/aaba9521b9c66ad9d524bcc064cd282ae41b5137.pdf |
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients | https://openreview.net/forum?id=AIgn9uwfcD1 | https://openreview.net/forum?id=AIgn9uwfcD1 | Milad Alizadeh,Shyam A. Tailor,Luisa M Zintgraf,Joost van Amersfoort,Sebastian Farquhar,Nicholas Donald Lane,Yarin Gal | ICLR 2022,Poster | Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for training and inference. However, current methods are insufficient to enable this optimization and lead to a large degradation in model performance. In this paper, we identify a fundamental limitation in the formulation of current methods, namely that their saliency criteria look at a single step at the start of training without taking into account the trainability of the network. While pruning iteratively and gradually has been shown to improve pruning performance, explicit consideration of the training stage that will immediately follow pruning has so far been absent from the computation of the saliency criterion. To overcome the short-sightedness of existing methods, we propose Prospect Pruning (ProsPr), which uses meta-gradients through the first few steps of optimization to determine which weights to prune. ProsPr combines an estimate of the higher-order effects of pruning on the loss and the optimization trajectory to identify the trainable sub-network. Our method achieves state-of-the-art pruning performance on a variety of vision classification tasks, with less data and in a single shot compared to existing pruning-at-initialization methods. | https://openreview.net/pdf/1dfea35c91657067e141f26d76aa2c2c7e96bcc6.pdf |
CoMPS: Continual Meta Policy Search | https://openreview.net/forum?id=PVJ6j87gOHz | https://openreview.net/forum?id=PVJ6j87gOHz | Glen Berseth,Zhiwei Zhang,Grace Zhang,Chelsea Finn,Sergey Levine | ICLR 2022,Poster | We develop a new continual meta-learning method to address challenges in sequential multi-task learning. In this setting, the agent's goal is to achieve high reward over any sequence of tasks quickly. Prior meta-reinforcement learning algorithms have demonstrated promising results in accelerating the acquisition of new tasks. However, they require access to all tasks during training. Beyond simply transferring past experience to new tasks, our goal is to devise continual reinforcement learning algorithms that learn to learn, using their experience on previous tasks to learn new tasks more quickly. We introduce a new method, continual meta-policy search (CoMPS), that removes this limitation by meta-training in an incremental fashion, over each task in a sequence, without revisiting prior tasks. CoMPS continuously repeats two subroutines: learning a new task using RL and using the experience from RL to perform completely offline meta-learning to prepare for subsequent task learning. We find that CoMPS outperforms prior continual learning and off-policy meta-reinforcement methods on several sequences of challenging continuous control tasks. | https://openreview.net/pdf/3ca19a998e5306a18adb13bd58c7b521611ff1f2.pdf |
Generalized rectifier wavelet covariance models for texture synthesis | https://openreview.net/forum?id=ziRLU3Y2PN_ | https://openreview.net/forum?id=ziRLU3Y2PN_ | Antoine Brochard,Sixin Zhang,Stéphane Mallat | ICLR 2022,Poster | State-of-the-art maximum entropy models for texture synthesis are built from statistics relying on image representations defined by convolutional neural networks (CNN). Such representations capture rich structures in texture images, outperforming wavelet-based representations in this regard. However, conversely to neural networks, wavelets offer meaningful representations, as they are known to detect structures at multiple scales (e.g. edges) in images. In this work, we propose a family of statistics built upon non-linear wavelet based representations, that can be viewed as a particular instance of a one-layer CNN, using a generalized rectifier non-linearity. These statistics significantly improve the visual quality of previous classical wavelet-based models, and allow one to produce syntheses of similar quality to state-of-the-art models, on both gray-scale and color textures. We further provide insights on memorization effects in these models.
| https://openreview.net/pdf/db6c8615f4a7a3851946620b267f795166d259df.pdf |
Towards Evaluating the Robustness of Neural Networks Learned by Transduction | https://openreview.net/forum?id=_5js_8uTrx1 | https://openreview.net/forum?id=_5js_8uTrx1 | Jiefeng Chen,Xi Wu,Yang Guo,Yingyu Liang,Somesh Jha | ICLR 2022,Poster | There has been emerging interest in using transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020; Wang et al., ArXiv 2021). Compared to traditional defenses, these defense mechanisms "dynamically learn" the model based on test-time input; and theoretically, attacking these defenses reduces to solving a bilevel optimization problem, which poses difficulty in crafting adaptive attacks. In this paper, we examine these defense mechanisms from a principled threat analysis perspective. We formulate and analyze threat models for transductive-learning based defenses, and point out important subtleties. We propose the principle of attacking model space for solving bilevel attack objectives, and present Greedy Model Space Attack (GMSA), an attack framework that can serve as a new baseline for evaluating transductive-learning based defenses. Through systematic evaluation, we show that GMSA, even with weak instantiations, can break previous transductive-learning based defenses, which were resilient to previous attacks, such as AutoAttack (Croce and Hein, ICML 2020). On the positive side, we report a somewhat surprising empirical result of "transductive adversarial training": Adversarially retraining the model using fresh randomness at the test time gives a significant increase in robustness against attacks we consider. | https://openreview.net/pdf/3ca51d07268ca31c7f5ccb9d049d08f06a99f051.pdf |
OBJECT DYNAMICS DISTILLATION FOR SCENE DECOMPOSITION AND REPRESENTATION | https://openreview.net/forum?id=oJGDYQFKL3i | https://openreview.net/forum?id=oJGDYQFKL3i | Qu Tang,Xiangyu Zhu,Zhen Lei,Zhaoxiang Zhang | ICLR 2022,Poster | The ability to perceive scenes in terms of abstract entities is crucial for us to
achieve higher-level intelligence. Recently, several methods have been proposed
to learn object-centric representations of scenes with multiple objects, yet most
of which focus on static scenes. In this paper, we work on object dynamics and
propose Object Dynamics Distillation Network (ODDN), a framework that distillates explicit object dynamics (e.g., velocity) from sequential static representations. ODDN also builds a relation module to model object interactions. We verify
our approach on tasks of video reasoning and video prediction, which are two important evaluations for video understanding. The results show that the reasoning
model with visual representations of ODDN performs better in answering reasoning questions around physical events in a video compared to the previous state-of-the-art methods. The distilled object dynamics also could be used to predict
future video frames given two input frames, involving occlusion and objects collision. In addition, our architecture brings better segmentation quality and higher
reconstruction accuracy. | https://openreview.net/pdf/0ad29c6ff610a0ba752b90b3599e1008193b012f.pdf |
Practical Integration via Separable Bijective Networks | https://openreview.net/forum?id=NlObxR0rosG | https://openreview.net/forum?id=NlObxR0rosG | Christopher M Bender,Patrick Emmanuel,Michael K. Reiter,Junier Oliva | ICLR 2022,Poster | Neural networks have enabled learning over examples that contain thousands of dimensions.
However, most of these models are limited to training and evaluating on a finite collection of \textit{points} and do not consider the hypervolume in which the data resides.
Any analysis of the model's local or global behavior is therefore limited to very expensive or imprecise estimators.
We propose to formulate neural networks as a composition of a bijective (flow) network followed by a learnable, separable network.
This construction allows for learning (or assessing) over full hypervolumes with precise estimators at tractable computational cost via integration over the \textit{input space}.
We develop the necessary machinery, propose several practical integrals to use during training, and demonstrate their utility. | https://openreview.net/pdf/95fa2183421df6d966e9da9063e2c757245f7d49.pdf |
Self-Joint Supervised Learning | https://openreview.net/forum?id=zuqcmNVK4c2 | https://openreview.net/forum?id=zuqcmNVK4c2 | Navid Kardan,Mubarak Shah,Mitch Hill | ICLR 2022,Poster | Supervised learning is a fundamental framework used to train machine learning systems. A supervised learning problem is often formulated using an i.i.d. assumption that restricts model attention to a single relevant signal at a time when predicting. This contrasts with the human ability to actively use related samples as reference when making decisions. We hypothesize that the restriction to a single signal for each prediction in the standard i.i.d. framework contributes to well-known drawbacks of supervised learning: making overconfident predictions and vulnerability to overfitting, adversarial attacks, and out-of-distribution data. To address these limitations, we propose a new supervised learning paradigm called self-joint learning that generalizes the standard approach by modeling the joint conditional distribution of two observed samples, where each sample is an image and its label. Rather than assuming samples are independent, our models explicitly learn the sample-to-sample relation of conditional independence. Our framework can naturally incorporate auxiliary unlabeled data to further improve the performance. Experiments on benchmark image datasets show our method offers significant improvement over standard supervised learning in terms of accuracy, robustness against adversarial attacks, out-of-distribution detection, and overconfidence mitigation. | https://openreview.net/pdf/960211bfb6a9d646a3fe04a45ebc7a4670bbe0ec.pdf |
Rethinking Supervised Pre-Training for Better Downstream Transferring | https://openreview.net/forum?id=Jjcv9MTqhcq | https://openreview.net/forum?id=Jjcv9MTqhcq | Yutong Feng,Jianwen Jiang,Mingqian Tang,Rong Jin,Yue Gao | ICLR 2022,Poster | The pretrain-finetune paradigm has shown outstanding performance on many applications of deep learning, where a model is pre-trained on an upstream large dataset (e.g. ImageNet), and is then fine-tuned to different downstream tasks. Though for most cases, the pre-training stage is conducted based on supervised methods, recent works on self-supervised pre-training have shown powerful transferability and even outperform supervised pre-training on multiple downstream tasks. It thus remains an open question how to better generalize supervised pre- training model to downstream tasks. In this paper, we argue that the worse transferability of existing supervised pre-training methods arise from the negligence of valuable intra-class semantic difference. This is because these methods tend to push images from the same class close to each other despite of the large diversity in their visual contents, a problem to which referred as “overfit of upstream tasks”. To alleviate this problem, we propose a new supervised pre-training method based on Leave-One-Out K-Nearest-Neighbor, or LOOK for short. It relieves the problem of overfitting upstream tasks by only requiring each image to share its class label with most of its k nearest neighbors, thus allowing each class to exhibit a multi-mode distribution and consequentially preserving part of intra-class difference for better transferring to downstream tasks. We developed efficient implementation of the proposed method that scales well to large datasets. Experimental studies on multiple downstream tasks show that LOOK outperforms other state-of-the-art methods for supervised and self-supervised pre-training. | https://openreview.net/pdf/a343ccb79799f29dc7462f132c0c658e9714c2b3.pdf |
A Zest of LIME: Towards Architecture-Independent Model Distances | https://openreview.net/forum?id=OUz_9TiTv9j | https://openreview.net/forum?id=OUz_9TiTv9j | Hengrui Jia,Hongyu Chen,Jonas Guan,Ali Shahin Shamsabadi,Nicolas Papernot | ICLR 2022,Poster | Definitions of the distance between two machine learning models either characterize the similarity of the models' predictions or of their weights. While similarity of weights is attractive because it implies similarity of predictions in the limit, it suffers from being inapplicable to comparing models with different architectures. On the other hand, the similarity of predictions is broadly applicable but depends heavily on the choice of model inputs during comparison. In this paper, we instead propose to compute distance between black-box models by comparing their Local Interpretable Model-Agnostic Explanations (LIME). To compare two models, we take a reference dataset, and locally approximate the models on each reference point with linear models trained by LIME. We then compute the cosine distance between the concatenated weights of the linear models. This yields an approach that is both architecture-independent and possesses the benefits of comparing models in weight space. We empirically show that our method, which we call Zest, can be applied to two problems that require measurements of model similarity: detecting model stealing and machine unlearning. | https://openreview.net/pdf/01c303fb27c1a38f990545d60c831f0fa8f0f2b9.pdf |
Meta-Imitation Learning by Watching Video Demonstrations | https://openreview.net/forum?id=KTPuIsx4pmo | https://openreview.net/forum?id=KTPuIsx4pmo | Jiayi Li,Tao Lu,Xiaoge Cao,Yinghao Cai,Shuo Wang | ICLR 2022,Poster | Meta-Imitation Learning is a promising technique for the robot to learn a new task from observing one or a few human demonstrations. However, it usually requires a significant number of demonstrations both from humans and robots during the meta-training phase, which is a laborious and hard work for data collection, especially in recording the actions and specifying the correspondence between human and robot. In this work, we present an approach of meta-imitation learning by watching video demonstrations from humans. In comparison to prior works, our approach is able to translate human videos into practical robot demonstrations and train the meta-policy with adaptive loss based on the quality of the translated data. Our approach relies only on human videos and does not require robot demonstration, which facilitates data collection and is more in line with human imitation behavior. Experiments reveal that our method achieves the comparable performance to the baseline on fast learning a set of vision-based tasks through watching a single video demonstration. | https://openreview.net/pdf/c676abf58096dad39810f6901eb441d104797d02.pdf |
Understanding Intrinsic Robustness Using Label Uncertainty | https://openreview.net/forum?id=6ET9SzlgNX | https://openreview.net/forum?id=6ET9SzlgNX | Xiao Zhang,David Evans | ICLR 2022,Poster | A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task. A line of research has made some progress towards this goal by studying the concentration of measure, but we argue standard concentration fails to fully characterize the intrinsic robustness of a classification problem since it ignores data labels which are essential to any classification task. Building on a novel definition of label uncertainty, we empirically demonstrate that error regions induced by state-of-the-art models tend to have much higher label uncertainty than randomly-selected subsets. This observation motivates us to adapt a concentration estimation algorithm to account for label uncertainty, resulting in more accurate intrinsic robustness measures for benchmark image classification problems. | https://openreview.net/pdf/7fac6994dd2b9e6641f9bacef039b34d4d5dd804.pdf |
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | https://openreview.net/forum?id=_QLmakITKg | https://openreview.net/forum?id=_QLmakITKg | Junyuan Hong,Haotao Wang,Zhangyang Wang,Jiayu Zhou | ICLR 2022,Poster | Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In many practical FL scenarios, participants have heterogeneous resources due to disparities in hardware and inference dynamics that require quickly loading models of different sizes and levels of robustness. The heterogeneity and dynamics together impose significant challenges to existing FL approaches and thus greatly limit FL's applicability. In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness. Specifically, we achieve customization by learning a set of base sub-networks of different sizes and robustness levels, which are later aggregated on-demand according to inference requirements. This split-mix strategy achieves customization with high efficiency in communication, storage, and inference. Extensive experiments demonstrate that our method provides better in-situ customization than the existing heterogeneous-architecture FL methods. Codes and pre-trained models are available: https://github.com/illidanlab/SplitMix. | https://openreview.net/pdf/98ee807a90553d41affe59b4484a0754e4bdb5d7.pdf |
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