mtybilly/Instruct2DS huggingface dataset:
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training
Authors: Tianlong Chen, Zhenyu Zhang, pengjun wang, Santosh Balachandra, Haoyu Ma, Zehao Wang, Zhangyang Wang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Recent studies demonstrate the deep networks, even robustified by the state-of-the-art adversarial training (AT), still suffer from large robust generalization gaps, in addition to the much more expensive training costs than standard training. In this paper, we investigate this intriguing problem from a new perspective, i.e., $\textit{injecting appropriate forms of sparsity}$ during adversarial training. We introduce two alternatives for sparse adversarial training: (i) $\textit{static sparsity}$, by leveraging recent results from the lottery ticket hypothesis to identify critical sparse subnetworks arising from the early training; (ii) $\textit{dynamic sparsity}$, by allowing the sparse subnetwork to adaptively adjust its connectivity pattern (while sticking to the same sparsity ratio) throughout training. We find both static and dynamic sparse methods to yield win-win: substantially shrinking the robust generalization gap and alleviating the robust overfitting, meanwhile significantly saving training and inference FLOPs. Extensive experiments validate our proposals with multiple network architectures on diverse datasets, including CIFAR-10/100 and Tiny-ImageNet. For example, our methods reduce robust generalization gap and overfitting by $34.44\%$ and $4.02\%$, with comparable robust/standard accuracy boosts and $87.83\%$/$87.82\%$ training/inference FLOPs savings on CIFAR-100 with ResNet-18. Besides, our approaches can be organically combined with existing regularizers, establishing new state-of-the-art results in AT. All codes are included.
On the Existence of Universal Lottery Tickets
Authors: Rebekka Burkholz, Nilanjana Laha, Rajarshi Mukherjee, Alkis Gotovos
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation. Recent work has experimentally observed that some of these tickets can be practically reused across a variety of tasks, hinting at some form of universality. We formalize this concept and theoretically prove that not only do such universal tickets exist but they also do not require further training. Our proofs introduce a couple of technical innovations related to pruning for strong lottery tickets, including extensions of subset sum results and a strategy to leverage higher amounts of depth. Our explicit sparse constructions of universal function families might be of independent interest, as they highlight representational benefits induced by univariate convolutional architectures.
Evaluating Model-Based Planning and Planner Amortization for Continuous Control
Authors: Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. We show that MPC with learned proposals and models (trained on the fly or transferred from related tasks) can significantly improve performance and data efficiency with respect to model-free methods. However, we find that well-tuned model-free agents are strong baselines even for high DoF control problems. Finally, we show that it is possible to distil a model-based planner into a policy that amortizes the planning computation without any loss of performance.
Auto-Transfer: Learning to Route Transferable Representations
Authors: Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications. Existing approaches typically constrain the target deep neural network (DNN) feature representations to be close to the source DNNs feature representations, which can be limiting. We, in this paper, propose a novel adversarial multi-armed bandit approach that automatically learns to route source representations to appropriate target representations following which they are combined in meaningful ways to produce accurate target models. We see upwards of 5\% accuracy improvements compared with the state-of-the-art knowledge transfer methods on four benchmark (target) image datasets CUB200, Stanford Dogs, MIT67, and Stanford40 where the source dataset is ImageNet. We qualitatively analyze the goodness of our transfer scheme by showing individual examples of the important features focused on by our target network at different layers compared with the (closest) competitors. We also observe that our improvement over other methods is higher for smaller target datasets making it an effective tool for small data applications that may benefit from transfer learning.
Equivariant Graph Mechanics Networks with Constraints
Authors: Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Learning to reason about relations and dynamics over multiple interacting objects is a challenging topic in machine learning. The challenges mainly stem from that the interacting systems are exponentially-compositional, symmetrical, and commonly geometrically-constrained.
Current methods, particularly the ones based on equivariant Graph Neural Networks (GNNs), have targeted on the first two challenges but remain immature for constrained systems. 
In this paper, we propose Graph Mechanics Network (GMN) which is combinatorially efficient, equivariant and constraint-aware. The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object. In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics. Moreover, to allow equivariant message passing in GMN, we have developed a general form of orthogonality-equivariant functions, given that the dynamics of constrained systems are more complicated than the unconstrained counterparts. Theoretically, the proposed equivariant formulation is proved to be universally expressive under certain conditions. Extensive experiments  support the advantages of GMN compared to the state-of-the-art GNNs in terms of prediction accuracy, constraint satisfaction and data efficiency on the simulated systems consisting of particles, sticks and hinges, as well as two real-world datasets for molecular dynamics prediction and human motion capture.
RvS: What is Essential for Offline RL via Supervised Learning?
Authors: Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. When does this hold true, and which algorithmic components are necessary? Through extensive experiments, we boil supervised learning for offline RL down to its essential elements. In every environment suite we consider, simply maximizing likelihood with a two-layer feedforward MLP is competitive with state-of-the-art results of substantially more complex methods based on TD learning or sequence modeling with Transformers. Carefully choosing model capacity (e.g., via regularization or architecture) and choosing which information to condition on (e.g., goals or rewards) are critical for performance. These insights serve as a field guide for practitioners doing Reinforcement Learning via Supervised Learning (which we coin RvS learning). They also probe the limits of existing RvS methods, which are comparatively weak on random data, and suggest a number of open problems.
Looking Back on Learned Experiences  For Class/task Incremental Learning
Authors: Mozhgan PourKeshavarzi, Guoying Zhao, Mohammad Sabokrou
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Classical deep neural networks are limited in their ability to learn from emerging streams of training data. When trained sequentially on new or evolving tasks, their performance degrades sharply, making them inappropriate in real-world use cases. Existing methods tackle it by either storing old data samples or only updating a parameter set of deep neural networks, which, however, demands a large memory budget or spoils the flexibility of models to learn the incremented task distribution. In this paper, we shed light on an on-call transfer set to provide past experiences whenever a new task arises in the data stream. In particular, we propose a Cost-Free Incremental Learning (CF-IL) not only to replay past experiences the model has learned but also to perform this in a cost free manner. Towards this end, we introduced a memory recovery paradigm in which we query the network to synthesize past exemplars whenever a new task emerges. Thus, our method needs no extra memory for data buffering or network growing, besides calls the proposed memory recovery paradigm to provide past exemplars, named a transfer set in order to mitigate catastrophically forgetting the former tasks in the Incremental Learning (IL) setup. Moreover, in contrast with recently proposed methods, the suggested paradigm does not desire a parallel architecture since it only relies on the learner network. Compared to the state-of-the-art data techniques without buffering past data samples, CF-IL demonstrates significantly better performance on the well-known datasets whether a task oracle is available in test time (Task-IL) or not (Class-IL).
Transformer Embeddings of Irregularly Spaced Events and Their Participants
Authors: Hongyuan Mei, Chenghao Yang, Jason Eisner
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events. To handle complex domains with many event types, Mei et al. (2020a) further consider a setting in which each event in the sequence updates a deductive database of facts (via domain-specific pattern-matching rules); future events are then conditioned on the database contents. They show how to convert such a symbolic system into a neuro-symbolic continuous-time generative model, in which each database fact and possible event has a time-varying embedding that is derived from its symbolic provenance. 

In this paper, we modify both models, replacing their recurrent LSTM-based architectures with flatter attention-based architectures (Vaswani et al., 2017), which are simpler and more parallelizable. This does not appear to hurt our accuracy, which is comparable to or better than that of the original models as well as (where applicable) previous attention-based methods (Zuo et al., 2020; Zhang et al., 2020a).
Language-driven Semantic Segmentation
Authors: Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, Rene Ranftl
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., ``grass'' or ``building'') together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is trained with a contrastive objective to align pixel embeddings to the text embedding of the corresponding semantic class. The text embeddings provide a flexible label representation in which semantically similar labels map to similar regions in the embedding space (e.g., ``cat'' and ``furry''). This allows LSeg to generalize to previously unseen categories at test time, without retraining or even requiring a single additional training sample. We demonstrate that our approach achieves highly competitive zero-shot performance compared to existing zero- and few-shot semantic segmentation methods, and even matches the accuracy of traditional segmentation algorithms when a fixed label set is provided. Code and demo are available at https://github.com/isl-org/lang-seg.
Tracking the risk of a deployed model and detecting harmful distribution shifts
Authors: Aleksandr Podkopaev, Aaditya Ramdas
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: When deployed in the real world, machine learning models inevitably encounter changes in the data distribution, and certain---but not all---distribution shifts could result in significant performance degradation. In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does not degrade substantially, making interventions by a human expert (or model retraining) unnecessary.  While several works have developed tests for distribution shifts, these typically either use non-sequential methods, or detect arbitrary shifts (benign or harmful), or both. We argue that a sensible method for firing off a warning has to both (a) detect harmful shifts while ignoring benign ones, and (b) allow continuous monitoring of model performance without increasing the false alarm rate. In this work, we design simple sequential tools for testing if the difference between source (training) and target (test) distributions leads to a significant increase in a risk function of interest, like accuracy or calibration. Recent advances in constructing time-uniform confidence sequences allow efficient aggregation of statistical evidence accumulated during the tracking process. The designed framework is applicable in settings where (some) true labels are revealed after the prediction is performed, or when batches of labels become available in a delayed fashion. We demonstrate the efficacy of the proposed framework through an extensive empirical study on a collection of simulated and real datasets.
Sample and Computation Redistribution for Efficient Face Detection
Authors: Jia Guo, Jiankang Deng, Alexandros Lattas, Stefanos Zafeiriou
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Although tremendous strides have been made in uncontrolled face detection, accurate face detection with a low computation cost remains an open challenge. In this paper, we point out that computation distribution and scale augmentation are the keys to detecting small faces from low-resolution images. Motivated by these observations, we introduce two simple but effective methods: (1) Computation Redistribution (CR), which reallocates the computation between the backbone, neck and head of the model; and (2) Sample Redistribution (SR), which augments training samples for the most needed stages. The proposed Sample and Computation Redistribution for Face Detection (SCRFD) is implemented by a random search in a meticulously designed search space. Extensive experiments conducted on WIDER FACE demonstrate the state-of-the-art accuracy-efficiency trade-off for the proposed SCRFD family across a wide range of compute regimes. In particular, SCRFD-34GF outperforms the best competitor, TinaFace, by $4.78\%$ (AP at hard set) while being more than 3$\times$ faster on GPUs with VGA-resolution images. Code is available at: https://github.com/deepinsight/insightface/tree/master/detection/scrfd.
Exploring extreme parameter compression for pre-trained language models
Authors: Benyou Wang, Yuxin Ren, Lifeng Shang, Xin Jiang, Qun Liu
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Recent work explored the potential of large-scale Transformer-based pre-trained models, especially Pre-trained Language Models (PLMs) in natural language processing. This raises many concerns from various perspectives, e.g.,  financial costs and carbon emissions. 
Compressing PLMs like BERT with negligible performance loss for faster inference and cheaper deployment has attracted much attention. In this work, we aim to explore larger compression ratios for PLMs, among which tensor decomposition is a potential but under-investigated one. By comparing existing decomposition methods, Tucker decomposition is found to be parameter-efficient for compression.  Two decomposition and reconstruction protocols are further proposed to improve the effectiveness and efficiency of Tucker decomposition in parameter compression.
Our compressed BERT with ${1}/{7}$ parameters in Transformer layers performs on-par with,  sometimes slightly better than the original BERT in GLUE benchmark. A tiny version achieves  96.7\%  performance of  BERT-base with $ {1}/{48} $ encoder parameters (i.e., less than 2M parameters excluding the embedding layer) and  \textbf{$2.7 \times$} faster on inference. To show that the proposed method is orthogonal to existing compression methods like knowledge distillation, we also explore the benefit of the proposed method on a distilled BERT.
Optimal Representations for Covariate Shift
Authors: Yangjun Ruan, Yann Dubois, Chris J. Maddison
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Machine learning systems often experience a distribution shift between training and testing. In this paper, we introduce a simple variational objective whose optima are exactly the set of all representations on which risk minimizers are guaranteed to be robust to any distribution shift that preserves the Bayes predictor, e.g., covariate shifts. Our objective has two components. First, a representation must remain discriminative for the task, i.e., some predictor must be able to simultaneously minimize the source and target risk. Second, the representation's marginal support needs to be the same across source and target. We make this practical by designing self-supervised objectives that only use unlabelled data and augmentations to train robust representations. 
Our objectives give insights into the robustness of CLIP, and further improve CLIP's representations to achieve SOTA results on DomainBed.
An Explanation of In-context Learning as Implicit Bayesian Inference
Authors: Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning.
Anti-Concentrated Confidence Bonuses For Scalable Exploration
Authors: Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Intrinsic rewards play a central role in handling the exploration-exploitation tradeoff when designing sequential decision-making algorithms, in both foundational theory and state-of-the-art deep reinforcement learning. The LinUCB algorithm, a centerpiece of the stochastic linear bandits literature, prescribes an elliptical bonus which addresses the challenge of leveraging shared information in large action spaces. This bonus scheme cannot be directly transferred to high-dimensional exploration problems, however, due to the computational cost of maintaining the inverse covariance matrix of action features. We introduce anti-concentrated confidence bounds for efficiently approximating the elliptical bonus, using an ensemble of regressors trained to predict random noise from policy network-derived features. Using this approximation, we obtain stochastic linear bandit algorithms which obtain $\tilde O(d \sqrt{T})$ regret bounds for $\mathsf{poly}(d)$ fixed actions. We develop a practical variant that is competitive with contemporary intrinsic reward heuristics on Atari benchmarks.
Semi-relaxed Gromov-Wasserstein divergence and applications on graphs
Authors: Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Comparing structured objects such as graphs is a fundamental operation
involved in many learning tasks. To this end, the Gromov-Wasserstein (GW)
distance, based on Optimal Transport (OT), has proven to be successful in
handling the specific nature of the associated objects. More specifically,
through the nodes connectivity relations, GW operates on graphs, seen as
probability measures over specific spaces. At the core of OT is the idea of
conservation of mass, which imposes a coupling between all the nodes from
the two considered graphs. We argue in this paper that this property can be
detrimental for tasks such as graph dictionary or partition learning, and we
relax it by proposing a new semi-relaxed Gromov-Wasserstein divergence.
Aside from immediate computational benefits, we discuss its properties, and
show that it can lead to an efficient graph dictionary learning algorithm.
We empirically demonstrate its relevance for complex tasks on graphs such as
partitioning, clustering and completion.
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity
Authors: Byungseok Roh, JaeWoong Shin, Wuhyun Shin, Saehoon Kim
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: DETR is the first end-to-end object detector using a transformer encoder-decoder architecture and demonstrates competitive performance but low computational efficiency. The subsequent work, Deformable DETR, enhances the efficiency of DETR by replacing dense attention with deformable attention, which achieves 10x faster convergence and improved performance. Using the multiscale feature to ameliorate performance, however, the number of encoder queries increases by 20x compared to DETR, and the computation cost of the encoder attention remains a bottleneck. We observe that the encoder queries referenced by the decoder account for only 45% of the total, and find out the detection accuracy does not deteriorate significantly even if only the referenced queries are polished in the encoder block. Inspired by this observation, we propose Sparse DETR that selectively updates only the queries expected to be referenced by the decoder, thus help the model effectively detect objects. In addition, we show that applying an auxiliary detection loss on the selected queries in the encoder improves the performance while minimizing computational overhead. We validate that Sparse DETR achieves better performance than Deformable DETR even with only 10% encoder queries on the COCO dataset. Albeit only the encoder queries are sparsified, the total computation cost decreases by 38% and the frames per second (FPS) increases by 42% compared to Deformable DETR. Code will be released.
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics
Authors: Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Many real-world applications of reinforcement learning (RL) require the agent to deal with high-dimensional observations such as those generated from a megapixel camera. Prior work has addressed such problems with representation learning, through which the agent can provably extract endogenous, latent state information from raw observations and subsequently plan efficiently. However, such approaches can fail in the presence of temporally correlated noise in the observations, a phenomenon that is common in practice. We initiate the formal study of latent state discovery in the presence of such exogenous noise sources by proposing a new model, the Exogenous Block MDP (EX-BMDP), for rich observation RL. We start by establishing several negative results, by highlighting failure cases of prior representation learning based approaches. Then, we introduce the Predictive Path Elimination (PPE) algorithm, that learns a generalization of inverse dynamics and is provably sample and computationally efficient in EX-BMDPs when the endogenous state dynamics are near deterministic. The sample complexity of PPE depends polynomially on the size of the latent endogenous state space while not directly depending on the size of the observation space, nor the exogenous state space. We provide experiments on challenging exploration problems which show that our approach works empirically.
A Deep Variational Approach to Clustering Survival Data
Authors: Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi, Thomas Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E Vogt
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: In this work, we study the problem of clustering survival data — a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on clustering and mixture models for survival data in comprehensive experiments on a wide range of synthetic, semi-synthetic, and real-world datasets, including medical imaging data. Our method performs better at identifying clusters and is competitive at predicting survival times. Relying on novel generative assumptions, the proposed model offers a holistic perspective on clustering survival data and holds a promise of discovering subpopulations whose survival is regulated by different generative mechanisms.
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Authors: Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning literature. Albeit the promising results, naively training multiple deep neural networks and combining their predictions at inference leads to prohibitive computational costs and memory requirements. Recently proposed efficient ensemble approaches reach the performance of the traditional deep ensembles with significantly lower costs. However, the training resources required by these approaches are still at least the same as training a single dense model. In this work, we draw a unique connection between sparse neural network training and deep ensembles, yielding a novel efficient ensemble learning framework called $FreeTickets$. Instead of training multiple dense networks and averaging them, we directly train sparse subnetworks from scratch and extract diverse yet accurate subnetworks during this efficient, sparse-to-sparse training. Our framework, $FreeTickets$, is defined as the ensemble of these relatively cheap sparse subnetworks. Despite being an ensemble method, $FreeTickets$ has even fewer parameters and training FLOPs than a single dense model. This seemingly counter-intuitive outcome is due to the ultra training/inference efficiency of dynamic sparse training. $FreeTickets$ surpasses the dense baseline in all the following criteria: prediction accuracy, uncertainty estimation, out-of-distribution (OoD) robustness, as well as efficiency for both training and inference. Impressively, $FreeTickets$ outperforms the naive deep ensemble with ResNet50 on ImageNet using around only $1/5$ of the training FLOPs required by the latter. We have released our source code at https://github.com/VITA-Group/FreeTickets.
Vision-Based Manipulators Need to Also See from Their Hands
Authors: Kyle Hsu, Moo Jin Kim, Rafael Rafailov, Jiajun Wu, Chelsea Finn
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: We study how the choice of visual perspective affects learning and generalization in the context of physical manipulation from raw sensor observations. Compared with the more commonly used global third-person perspective, a hand-centric (eye-in-hand) perspective affords reduced observability, but we find that it consistently improves training efficiency and out-of-distribution generalization. These benefits hold across a variety of learning algorithms, experimental settings, and distribution shifts, and for both simulated and real robot apparatuses. However, this is only the case when hand-centric observability is sufficient; otherwise, including a third-person perspective is necessary for learning, but also harms out-of-distribution generalization. To mitigate this, we propose to regularize the third-person information stream via a variational information bottleneck. On six representative manipulation tasks with varying hand-centric observability adapted from the Meta-World benchmark, this results in a state-of-the-art reinforcement learning agent operating from both perspectives improving its out-of-distribution generalization on every task. While some practitioners have long put cameras in the hands of robots, our work systematically analyzes the benefits of doing so and provides simple and broadly applicable insights for improving end-to-end learned vision-based robotic manipulation.
Learning Temporally Causal Latent Processes from General Temporal Data
Authors: Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not uniquely recoverable in the most general case. In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures. We propose LEAP, a theoretically-grounded framework that extends Variational AutoEncoders (VAEs) by enforcing our conditions through proper constraints in causal process prior. Experimental results on various datasets demonstrate that temporally causal latent processes are reliably identified from observed variables under different dependency structures and that our approach considerably outperforms baselines that do not properly leverage history or nonstationarity information. This demonstrates that using temporal information to learn latent processes from their invertible nonlinear mixtures in an unsupervised manner, for which we believe our work is one of the first, seems promising even without sparsity or minimality assumptions.
Offline Reinforcement Learning with Value-based Episodic Memory
Authors: Xiaoteng Ma, Yiqin Yang, Hao Hu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, Bin Liang, Qihan Liu
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data. Most existing offline RL algorithms use regularization or constraints to suppress extrapolation error for actions outside the dataset. In this paper, we adopt a different framework, which learns the V-function instead of the Q-function to naturally keep the learning procedure within the support of an offline dataset. To enable effective generalization while maintaining proper conservatism in offline learning, we propose Expectile V-Learning (EVL), which smoothly interpolates between the optimal value learning and behavior cloning. Further, we introduce implicit planning along offline trajectories to enhance learned V-values and accelerate convergence. Together, we present a new offline method called Value-based Episodic Memory (VEM). We provide theoretical analysis for the convergence properties of our proposed VEM method, and empirical results in the D4RL benchmark show that our method achieves superior performance in most tasks, particularly in sparse-reward tasks.
Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning
Authors: Shaofeng Zhang, Feng Zhu, Junchi Yan, Rui Zhao, Xiaokang Yang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: For self-supervised contrastive learning, models can easily collapse and generate trivial constant solutions. The issue has been mitigated by recent improvement on objective design, which however often requires square complexity either for the size of instances ($\mathcal{O}(N^{2})$) or feature dimensions ($\mathcal{O}(d)^2$). To prevent such collapse, we develop two novel methods by decorrelating on different dimensions on the instance embedding stacking matrix, i.e., \textbf{I}nstance-wise (ICL) and \textbf{F}eature-wise (FCL) \textbf{C}ontrastive \textbf{L}earning. The proposed two methods (FCL, ICL) can be combined synthetically, called Zero-CL, where ``Zero'' means negative samples are \textbf{zero} relevant, which allows Zero-CL to completely discard negative pairs i.e., with \textbf{zero} negative samples. Compared with previous methods, Zero-CL mainly enjoys three advantages: 1) Negative free in symmetric architecture. 2) By whitening transformation, the correlation of the different features is equal to zero, alleviating information redundancy. 3) Zero-CL remains original information to a great extent after transformation, which improves the accuracy against other whitening transformation techniques. Extensive experimental results on CIFAR-10/100 and ImageNet show that Zero-CL outperforms or is on par with state-of-the-art symmetric contrastive learning methods.
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
Authors: Ofir Press, Noah Smith, Mike Lewis
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Since the introduction of the transformer model by Vaswani et al. (2017), a fundamental question has yet to be answered: how does a model achieve extrapolation at inference time for sequences that are longer than it saw during training? We first show that extrapolation can be enabled by simply changing the position representation method, though we find that current methods do not allow for efficient extrapolation. We therefore introduce a simpler and more efficient position method, Attention with Linear Biases (ALiBi). ALiBi does not add positional embeddings to word embeddings;  instead, it biases query-key attention scores with a penalty that is proportional to their distance. We show that this method trains a 1.3 billion parameter model on input sequences of length 1024 that extrapolates to input sequences of length 2048, achieving the same perplexity as a sinusoidal position embedding model trained on inputs of length 2048 but training 11% faster and using 11% less memory. ALiBi's inductive bias towards recency also leads it to outperform multiple strong position methods on the WikiText-103 benchmark.
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space.
Authors: Seyed Omid Davoudi, Majid Komeili
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Recent advances in machine learning have brought opportunities for the ever-increasing use of AI in the real world. This has created concerns about the black-box nature of many of the most recent machine learning approaches. In this work, we propose an interpretable neural network that leverages metric and prototype learning for classification tasks. It encodes its own explanations and provides an improved case-based reasoning through learning prototypes in an embedding space learned by a probabilistic nearest neighbor rule. Through experiments, we demonstrated the effectiveness of the proposed method in both performance and the accuracy of the explanations provided.
A global convergence theory for deep ReLU implicit networks via over-parameterization
Authors: Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, Hongyang Gao
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rule of many commonly used neural network architectures. Its prediction rule is provided implicitly based on the solution of an equilibrium equation. Although a line of recent empirical studies has demonstrated its superior performances, the theoretical understanding of implicit neural networks is limited. In general, the equilibrium equation may not be well-posed during the training. As a result, there is no guarantee that a vanilla (stochastic) gradient descent (SGD) training nonlinear implicit neural networks can converge. This paper fills the gap by analyzing the gradient flow of Rectified Linear Unit (ReLU) activated implicit neural networks. For an $m$ width implicit neural network with ReLU activation and $n$ training samples, we show that a randomly initialized gradient descent converges to a global minimum at a linear rate for the square loss function if the implicit neural network is over-parameterized. It is worth noting that, unlike existing works on the convergence of (S)GD on finite-layer over-parameterized neural networks, our convergence results hold for implicit neural networks, where the number of layers is infinite.
Understanding the Variance Collapse of SVGD in High Dimensions
Authors: Jimmy Ba, Murat A Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Stein variational gradient descent (SVGD) is a deterministic inference algorithm that evolves a set of particles to fit a target distribution. Despite its computational efficiency, SVGD often underestimates the variance of the target distribution in high dimensions. In this work we attempt to explain the variance collapse in SVGD. On the qualitative side, we compare the SVGD update with gradient descent on the maximum mean discrepancy (MMD) objective; we observe that the variance collapse phenomenon relates to the bias from deterministic updates present in the "driving force" of SVGD, and empirically verify that removal of such bias leads to more accurate variance estimation. On the quantitative side, we demonstrate that the variance collapse of SVGD can be accurately predicted in the proportional asymptotic limit, i.e., when the number of particles $n$ and dimensions $d$ diverge at the same rate. In particular, for learning high-dimensional isotropic Gaussians, we derive the exact equilibrium variance for both SVGD and MMD-descent under certain near-orthogonality assumption on the converged particles, and confirm that SVGD suffers from the "curse of dimensionality".
Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
Authors: Xi Lin, Zhiyuan Yang, Qingfu Zhang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Multiobjective combinatorial optimization (MOCO) problems can be found in many real-world applications. However, exactly solving these problems would be very challenging, particularly when they are NP-hard. Many handcrafted heuristic methods have been proposed to tackle different MOCO problems over the past decades. In this work, we generalize the idea of neural combinatorial optimization, and develop a learning-based approach to approximate the whole Pareto set for a given MOCO problem without further search procedure. We propose a single preference-conditioned model to directly generate approximate Pareto solutions for any trade-off preference, and design an efficient multiobjective reinforcement learning algorithm to train this model. Our proposed method can be treated as a learning-based extension for the widely-used decomposition-based multiobjective evolutionary algorithm (MOEA/D). It uses a single model to accommodate all the possible preferences, whereas other methods use a finite number of solutions to approximate the Pareto set. Experimental results show that our proposed method significantly outperforms some other methods on the multiobjective traveling salesman problem, multiobjective vehicle routing problem, and multiobjective knapsack problem in terms of solution quality, speed, and model efficiency.
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
Authors: Qilong Zhang, Xiaodan Li, YueFeng Chen, Jingkuan Song, Lianli Gao, Yuan He, Hui Xue'
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Adversarial examples have posed a severe threat to deep neural networks due to their transferable nature. Currently, various works have paid great efforts to enhance the cross-model transferability, which mostly assume the substitute model is trained in the same domain as the target model.
However, in reality, the relevant information of the deployed model is unlikely to leak.
Hence, it is vital to build a more practical black-box threat model to overcome this limitation and evaluate the vulnerability of deployed models.
In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks). Specifically, we leverage a generative model to learn the adversarial function for disrupting low-level features of input images. 
Based on this framework, we further propose two variants to narrow the gap between the source and target domains from the data and model perspectives, respectively. Extensive experiments on coarse-grained and fine-grained domains demonstrate the effectiveness of our proposed methods. Notably,
our methods outperform state-of-the-art approaches by up to 7.71\% (towards coarse-grained domains) and 25.91\% (towards fine-grained domains) on average. Our code is available at \url{https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack}.
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data
Authors: Yaxing Wang, Joost van de weijer, Lu Yu, SHANGLING JUI
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data. 
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN)  we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID.
StyleAlign: Analysis and Applications of Aligned StyleGAN Models
Authors: Zongze Wu, Yotam Nitzan, Eli Shechtman, Dani Lischinski
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: In this paper, we perform an in-depth study of the properties and applications of aligned generative models.
We refer to two models as aligned if they share the same architecture, and one of them (the child) is obtained from the other (the parent) via fine-tuning to another domain, a common practice in transfer learning. Several works already utilize some basic properties of aligned StyleGAN models to perform image-to-image translation. Here, we perform the first detailed exploration of model alignment, also focusing on StyleGAN. First, we empirically analyze aligned models and provide answers to important questions regarding their nature. In particular, we find that the child model's latent spaces are semantically aligned with those of the parent, inheriting incredibly rich semantics, even for distant data domains such as human faces and churches. Second, equipped with this better understanding, we leverage aligned models to solve a diverse set of tasks. In addition to image translation, we demonstrate fully automatic cross-domain image morphing. We further show that zero-shot vision tasks may be performed in the child domain, while relying exclusively on supervision in the parent domain. We demonstrate qualitatively and quantitatively that our approach yields state-of-the-art results, while requiring only simple fine-tuning and inversion.