mtybilly/Instruct2DS huggingface dataset:
Language modeling via stochastic processes
Authors: Esin Durmus, Noah Goodman, Tatsunori Hashimoto Rose E Wang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Modern language models can generate high-quality short texts. However, they often meander or are incoherent when generating longer texts. These issues arise from the next-token-only language modeling objective. To address these issues, we introduce Time Control (TC), a language model that implicitly plans via a latent stochastic process. TC does this by learning a representation which maps the dynamics of how text changes in a document to the dynamics of a stochastic process of interest. Using this representation, the language model can generate text by first implicitly generating a document plan via a stochastic process, and then generating text that is consistent with this latent plan. Compared to domain-specific methods and fine-tuning GPT2 across a variety of text domains, TC improves performance on text infilling and discourse coherence. On long text generation settings, TC preserves the text structure both in terms of ordering (up to +40% better) and text length consistency (up to +17% better).  Human evaluators also prefer TC's output 28.6% more than the baselines.
IntSGD: Adaptive Floatless Compression of Stochastic Gradients
Authors: Bokun Wang, Dmitry Kovalev, Peter Richtárik Konstantin Mishchenko
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: We propose a family of adaptive integer compression operators for distributed Stochastic Gradient Descent (SGD) that do not communicate a single float. This is achieved by multiplying floating-point vectors with a number known to every device and then rounding to integers. In contrast to the prior work on integer compression for SwitchML by (Sapio et al., 2021), our IntSGD method is provably convergent and computationally cheaper as it estimates the scaling of vectors adaptively. Our theory shows that the iteration complexity of IntSGD matches that of SGD up to constant factors for both convex and non-convex, smooth and non-smooth functions, with and without overparameterization. Moreover, our algorithm can also be tailored for the popular all-reduce primitive and shows promising empirical performance.
Disentanglement Analysis with Partial Information Decomposition
Authors: Issei Sato Seiya Tokui
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: We propose a framework to analyze how multivariate representations disentangle ground-truth generative factors. A quantitative analysis of disentanglement has been based on metrics designed to compare how one variable explains each generative factor. Current metrics, however, may fail to detect entanglement that involves more than two variables, e.g., representations that duplicate and rotate generative factors in high dimensional spaces. In this work, we establish a framework to analyze information sharing in a multivariate representation with Partial Information Decomposition and propose a new disentanglement metric. This framework enables us to understand disentanglement in terms of uniqueness, redundancy, and synergy. We develop an experimental protocol to assess how increasingly entangled representations are evaluated with each metric and confirm that the proposed metric correctly responds to entanglement. Through experiments on variational autoencoders, we find that models with similar disentanglement scores have a variety of characteristics in entanglement, for each of which a distinct strategy may be required to obtain a disentangled representation.
Discrepancy-Based Active Learning for Domain Adaptation
Authors: François Deheeger, Mathilde MOUGEOT, Nicolas Vayatis Antoine de Mathelin
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions. Building on previous work by Mansour et al. (2009) we adapt the concept of discrepancy distance between source and target distributions to restrict the maximization over the hypothesis class to a localized class of functions which are performing accurate labeling on the source domain. We derive generalization error bounds for such active learning strategies in terms of Rademacher average and localized discrepancy for general loss functions which satisfy a regularity condition. A practical K-medoids algorithm that can address the case of large data set is inferred from the theoretical bounds. Our numerical experiments show that the proposed algorithm is competitive against other state-of-the-art active learning techniques in the context of domain adaptation, in particular on large data sets of around one hundred thousand images.
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification
Authors: Ji-Rong Wen Bing Su
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Explainable distances for sequence data depend on temporal alignment to tackle sequences with different lengths and local variances. Most sequence alignment methods infer the optimal alignment by solving an optimization problem under pre-defined feasible alignment constraints, which not only is time-consuming, but also makes end-to-end sequence learning intractable. In this paper, we propose a learnable sequence distance called Temporal Alignment Prediction (TAP). TAP employs a lightweight convolutional neural network to directly predict the optimal alignment between two sequences, so that only straightforward calculations are required and no optimization is involved in inference. TAP can be applied in different distance-based machine learning tasks. For supervised sequence representation learning, we show that TAP trained with various metric learning losses achieves completive performances with much faster inference speed. For few-shot action classification, we apply TAP as the distance measure in the metric learning-based episode-training paradigm. This simple strategy achieves comparable results with state-of-the-art few-shot action recognition methods.
Visual hyperacuity with moving sensor and recurrent neural computations
Authors: Or Ram, Eldad Assa, Michael Kreiserman, Ehud Ahissar Alexander Rivkind
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Dynamical phenomena, such as recurrent neuronal activity  and perpetual motion of the eye, are typically overlooked in models of bottom-up visual perception. Recent experiments suggest that tiny inter-saccadic eye motion ("fixational drift") enhances visual  acuity beyond the limit imposed by the density of retinal photoreceptors. Here we hypothesize that such an enhancement is enabled by recurrent neuronal computations in early visual areas. Specifically, we explore a setting involving a low-resolution dynamical sensor that moves with respect to a static scene, with drift-like tiny steps. This setting mimics a dynamical eye viewing objects in perceptually-challenging conditions. The dynamical sensory input is classified by a convolutional neural network with recurrent connectivity added to its lower layers, in analogy to recurrent connectivity in early visual areas.  Applying our system to CIFAR-10 and CIFAR-100 datasets down-sampled via 8x8 sensor, we found that (i) classification accuracy, which is drastically reduced by this down-sampling, is mostly restored to its 32x32 baseline level when using a moving sensor and recurrent connectivity, (ii) in this setting, neurons in the early layers exhibit a wide repertoire of selectivity patterns, spanning the spatiotemporal selectivity space, with neurons preferring different combinations of spatial and temporal patterning, and (iii) curved sensor's trajectories improve  visual acuity compared to straight trajectories, echoing recent experimental findings involving eye-tracking in challenging conditions. Our work sheds light on the possible role of recurrent connectivity in early vision as well as the roles of fixational drift and temporal-frequency selective cells in the visual system. It also proposes a solution for artificial image recognition in settings with limited resolution and multiple time samples, such as in edge AI applications.
BEiT: BERT Pre-Training of Image Transformers
Authors: Li Dong, Songhao Piao, Furu Wei Hangbo Bao
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e., image patches (such as 16 x 16 pixels), and visual tokens (i.e., discrete tokens). We first ``tokenize'' the original image into visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder. Experimental results on image classification and semantic segmentation show that our model achieves competitive results with previous pre-training methods.
Topological Graph Neural Networks
Authors: Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt Max Horn
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated into any type of GNN and is strictly more expressive (in terms the Weisfeiler–Lehman graph isomorphism test) than message-passing GNNs. Augmenting GNNs with TOGL leads to improved predictive performance for graph and node classification tasks, both on synthetic data sets, which can be classified by humans using their topology but not by ordinary GNNs, and on real-world data.
Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach
Authors: Haibo Yang, Mingyi Hong, Jia Liu, Hoi To Wai, Sijia Liu Prashant Khanduri
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: 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.
Neural Network Approximation based on Hausdorff distance of Tropical Zonotopes
Authors: Georgios Smyrnis, George Retsinas, Petros Maragos Panagiotis Misiakos
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: In this work we theoretically contribute to neural network approximation by providing a novel tropical geometrical viewpoint to structured neural network compression. In particular, we show that the approximation error between two neural networks with ReLU activations and one hidden layer depends on the Hausdorff distance of the tropical zonotopes of the networks. This theorem comes as a first step towards a purely geometrical interpretation of neural network approximation. Based on this theoretical contribution, we propose geometrical methods that employ the K-means algorithm to compress the fully connected parts of ReLU activated deep neural networks. We analyze the error bounds of our algorithms theoretically based on our approximation theorem and evaluate them empirically on neural network compression. Our experiments follow a proof-of-concept strategy and indicate that our geometrical tools achieve improved performance over relevant tropical geometry techniques and can be competitive against non-tropical methods.
PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Authors: Yanfei Dong, Wee Sun Lee Mohammed Haroon Dupty
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Message passing Graph Neural Networks (GNNs) are known to be limited in expressive power by the 1-WL color-refinement test for graph isomorphism. Other more expressive models either are computationally expensive or need preprocessing to extract structural features from the graph. In this work, we propose to make GNNs universal by guiding the learning process with exact isomorphism solver techniques which operate on the paradigm of $\textit{Individualization and refinement}$ (IR), a method to artificially introduce asymmetry and further refine the coloring when 1-WL stops. Isomorphism solvers generate a search-tree of colorings whose leaves uniquely identify the graph. However, the tree grows exponentially large and needs hand-crafted pruning techniques which are not desirable from a learning perspective. We take a probabilistic view and approximate the search tree of colorings ( i.e. embeddings) by sampling multiple paths from root to leaves of the search-tree. To learn more discriminative representations, we guide the sampling process with $\textit{particle filter}$ updates, a principled approach for sequential state estimation. Our algorithm is end-to-end differentiable, can be applied with any GNN as backbone and learns richer graph representations with only linear increase in runtime. Experimental evaluation shows that our approach consistently outperforms leading GNN models on both synthetic benchmarks for isomorphism detection as well as real-world datasets.
Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation
Authors: Alex Rogozhnikov
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Tensor computations underlie modern scientific computing and deep learning.
A number of tensor frameworks emerged varying in execution model, hardware support, memory management, model definition, etc.
However, tensor operations in all frameworks follow the same paradigm.
Recent neural network architectures demonstrate demand for higher expressiveness of tensor operations.
The current paradigm is not suited to write readable, reliable, or easy-to-modify code for multidimensional tensor manipulations. 
Moreover, some commonly used operations do not provide sufficient checks and can break a tensor structure.
These mistakes are elusive as no tools or tests can detect them.
Independently, API discrepancies complicate code transfer between frameworks.
We propose einops notation: a uniform and generic way to manipulate tensor structure, that significantly improves code readability and flexibility by focusing on the structure of input and output tensors.
We implement einops notation in a Python package that efficiently supports multiple widely used frameworks and provides framework-independent minimalist API for tensor manipulations.
Leveraging unlabeled data to predict out-of-distribution performance
Authors: Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi Saurabh Garg
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions
that may cause performance drops. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a \emph{threshold} on the model's confidence, predicting accuracy as the fraction of unlabeled examples for which model confidence exceeds that threshold. ATC outperforms previous methods across several model architectures, types of distribution shifts (e.g., due to synthetic corruptions, dataset reproduction, or novel subpopulations), and datasets (\textsc{Wilds}-FMoW, ImageNet, \breeds, CIFAR, and MNIST).  In our experiments, ATC estimates target performance $2\text{--}4\times$ more accurately than prior methods. We also explore the theoretical foundations of the problem, proving that, in general, identifying the accuracy is just as hard as identifying the optimal predictor and thus, the efficacy of any method rests upon (perhaps unstated) assumptions on the nature of the shift. Finally, analyzing our method on some toy distributions, we provide insights concerning when it works.
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications
Authors: Yongyi Mao Ziqiao Wang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: 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.
Divergence-aware Federated Self-Supervised Learning
Authors: Yonggang Wen, Shuai Zhang Weiming Zhuang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulted from privacy constraints. Extensive attention has been paid to SSL approaches based on Siamese networks. However, such an effort has not yet revealed deep insights into various fundamental building blocks for the federated self-supervised learning (FedSSL) architecture. We aim to fill in this gap via in-depth empirical study and propose a new method to tackle the non-independently and identically distributed (non-IID) data problem of decentralized data. Firstly, we introduce a generalized FedSSL framework that embraces existing SSL methods based on Siamese networks and presents flexibility catering to future methods. In this framework, a server coordinates multiple clients to conduct SSL training and periodically updates local models of clients with the aggregated global model. Using the framework, our study uncovers unique insights of FedSSL: 1) stop-gradient operation, previously reported to be essential, is not always necessary in FedSSL; 2) retaining local knowledge of clients in FedSSL is particularly beneficial for non-IID data. Inspired by the insights, we then propose a new approach for model update, Federated Divergence-aware Exponential Moving Average update (FedEMA). FedEMA updates local models of clients adaptively using EMA of the global model, where the decay rate is dynamically measured by model divergence. Extensive experiments demonstrate that FedEMA outperforms existing methods by 3-4% on linear evaluation. We hope that this work will provide useful insights for future research.
Self-ensemble Adversarial Training for Improved Robustness
Authors: Yisen Wang Hongjun Wang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Due to numerous breakthroughs in real-world applications brought by machine intelligence, deep neural networks (DNNs) are widely employed in critical applications. However, predictions of DNNs are easily manipulated with imperceptible adversarial perturbations, which impedes the further deployment of DNNs and may result in profound security and privacy implications. By incorporating adversarial samples into the training data pool, adversarial training is the strongest principled strategy against various adversarial attacks among all sorts of defense methods. Recent works mainly focus on developing new loss functions or regularizers, attempting to find the unique optimal point in the weight space. But none of them taps the potentials of classifiers obtained from standard adversarial training, especially states on the searching trajectory of training. In this work, we are dedicated to the weight states of models through the training process and devise a simple but powerful \emph{Self-Ensemble Adversarial Training} (SEAT) method for yielding a robust classifier by averaging weights of history models. This considerably improves the robustness of the target model against several well known adversarial attacks, even merely utilizing the naive cross-entropy loss to supervise. We also discuss the relationship between the ensemble of predictions from different adversarially trained models and the prediction of weight-ensembled models, as well as provide theoretical and empirical evidence that the proposed self-ensemble method provides a smoother loss landscape and better robustness than both individual models and the ensemble of predictions from different classifiers. We further analyze a subtle but fatal issue in the general settings for the self-ensemble model, which causes the deterioration of the weight-ensembled method in the late phases.
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
Authors: Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang Shilong Liu
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer by layer. Using box coordinates not only helps using explicit positional priors to improve the query-to-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer by layer in a cascade manner. As a result, it leads to the best performance on the MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7\% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods. Code is available at \url{https://github.com/IDEA-opensource/DAB-DETR}.
OBJECT DYNAMICS DISTILLATION FOR SCENE DECOMPOSITION AND REPRESENTATION
Authors: Xiangyu Zhu, Zhen Lei, Zhaoxiang Zhang Qu Tang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: 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.
Self-Supervision Enhanced Feature Selection with Correlated Gates
Authors: Fergus Imrie, Mihaela van der Schaar Changhee Lee
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Discovering relevant input features for predicting a target variable is a key scientific question. However, in many domains, such as medicine and biology, feature selection is confounded by a scarcity of labeled samples coupled with significant correlations among features. In this paper, we propose a novel deep learning approach to feature selection that addresses both challenges simultaneously. First, we pre-train the network using unlabeled samples within a self-supervised learning framework by solving pretext tasks that require the network to learn informative representations from partial feature sets. Then, we fine-tune the pre-trained network to discover relevant features using labeled samples. During both training phases, we explicitly account for the correlation structure of the input features by generating correlated gate vectors from a multivariate Bernoulli distribution. Experiments on multiple real-world datasets including clinical and omics demonstrate that our model discovers relevant features that provide superior prediction performance compared to the state-of-the-art benchmarks in practical scenarios where there is often limited labeled data and high correlations among features.
CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture
Authors: Hongyi Sun, Nandita Vijaykumar Ruofan Liang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Implicit neural representations with multi-layer perceptrons (MLPs) have recently gained prominence for a wide variety of tasks such as novel view synthesis and 3D object representation and rendering. However, a significant challenge with these representations is that both training and inference with an MLP over a large number of input coordinates to learn and represent an image, video, or 3D object, require large amounts of computation and incur long processing times. In this work, we aim to accelerate inference and training of coordinate-based MLPs for implicit neural representations by proposing a new split MLP architecture, CoordX. With CoordX, the initial layers are split to learn each dimension of the input coordinates separately. The intermediate features are then fused by the last layers to generate the learned signal at the corresponding coordinate point. This significantly reduces the amount of computation required and leads to large speedups in training and inference, while achieving similar accuracy as the baseline MLP. This approach thus aims at first learning functions that are a decomposition of the original signal and then fusing them to generate the learned signal. Our proposed architecture can be generally used for many implicit neural representation tasks with no additional memory overheads. We demonstrate a speedup of up to 2.92x compared to the baseline model for image, video, and 3D shape representation and rendering tasks.
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness
Authors: Kun Huang, Oleh Rybkin, Dinesh Jayaraman Edward S. Hu
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data. How might we leverage data previously collected on another robot to reduce or even completely remove this need for robot-specific data? We propose a "robot-aware control" paradigm that achieves this by exploiting readily available knowledge about the robot. We then instantiate this in a robot-aware model-based RL policy by training modular dynamics models that couple a transferable, robot-aware world dynamics module with a robot-specific, potentially analytical, robot dynamics module. This also enables us to set up visual planning costs that separately consider the robot agent and the world. Our experiments on tabletop manipulation tasks with simulated and real robots demonstrate that these plug-in improvements dramatically boost the transferability of visual model-based RL policies, even permitting zero-shot transfer of visual manipulation skills onto new robots. Project website: https://www.seas.upenn.edu/~hued/rac
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
Authors: Utkarsh Soni, Mudit Verma, Siddharth Srivastava, Subbarao Kambhampati Sarath Sreedharan
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the {\em vocabulary mismatch} between the user and the AI system. This paper introduces methods for providing contrastive explanations in terms of user-specified concepts for sequential decision-making settings where the system's model of the task may be best represented as an inscrutable model. We do this by building partial symbolic models of a local approximation of the task that can be leveraged to answer the user queries. We test these methods on a popular Atari game (Montezuma's Revenge) and variants of Sokoban (a well-known planning benchmark) and report the results of user studies to evaluate whether people find explanations generated in this form useful.
On Incorporating Inductive Biases into VAEs
Authors: Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth Ning Miao
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: We explain why directly changing the prior can be a surprisingly ineffective mechanism for incorporating inductive biases into variational auto-encoders (VAEs), and introduce a simple and effective alternative approach: Intermediary Latent Space VAEs (InteL-VAEs). InteL-VAEs use an intermediary set of latent variables to control the stochasticity of the encoding process, before mapping these in turn to the latent representation using a parametric function that encapsulates our desired inductive bias(es). This allows us to impose properties like sparsity or clustering on learned representations, and incorporate human knowledge into the generative model. Whereas changing the prior only indirectly encourages behavior through regularizing the encoder, InteL-VAEs are able to directly enforce desired characteristics. Moreover, they bypass the computation and encoder design issues caused by non-Gaussian priors, while allowing for additional flexibility through training of the parametric mapping function. We show that these advantages, in turn, lead to both better generative models and better representations being learned.
Zero Pixel Directional Boundary by Vector Transform
Authors: Ajad Chhatkuli, Yun Liu, Ender Konukoglu, Luc Van Gool Edoardo Mello Rella
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Boundaries or contours are among the primary visual cues used by human and computer vision systems. One of the key problems in boundary detection is the loss formulation, which typically leads to class imbalance and, as a consequence, to thick boundaries which require non-differential post-processing steps to be thinned.
In this paper, we re-interpret boundaries as 1-D surfaces and formulate a one-to-one vector transform function that allows for training of boundary prediction completely avoiding the class imbalance issue. Specifically, we define the boundary representation at any point as the unit vector pointing to the closest boundary surface.
Our problem formulation leads to the estimation of direction as well as richer contextual information of the boundary, and, if desired, the availability of zero-pixel thin boundaries also at training time. Our method uses no hyper-parameter in the training loss and a fixed stable hyper-parameter at inference. We provide theoretical justification/discussions of the vector transform representation. We evaluate the proposed loss method using a standard architecture and show the excellent performance over other losses and representations on several datasets.
Diverse Client Selection for Federated Learning via Submodular Maximization
Authors: Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff Bilmes Ravikumar Balakrishnan
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: In every communication round of federated learning, a random subset of clients communicate  their  model  updates  back  to  the  server  which  then  aggregates them all.  The optimal size of this subset is not known and several studies have shown that typically random selection does not perform very well in terms of convergence, learning efficiency and fairness. We, in this paper, propose to select a small diverse subset of clients, namely those carrying representative gradient information, and we transmit only these updates to the server.  Our aim is for updating via only a subset to approximate updating via aggregating all client information. We achieve this by choosing a subset that maximizes a submodular facility location function defined over gradient space. We introduce “federated averaging with diverse client selection (DivFL)”. We provide a thorough analysis of its convergence in the heterogeneous setting and apply it both to synthetic and to real datasets. Empirical results show several benefits to our approach including improved learning efficiency, faster convergence and also more uniform (i.e., fair) performance across clients. We further show a communication-efficient version of DivFL that can still outperform baselines on the above metrics.
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference
Authors: Dahyun Kim, Jung-Woo Ha, Jonghyun Choi Hyunseo Koh
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Despite rapid advances in continual learning, a large body of research is devoted to improving performance in the existing setups.
While a handful of work do propose new continual learning setups, they still lack practicality in certain aspects.
For better practicality, we first propose a novel continual learning setup that is online, task-free, class-incremental, of blurry task boundaries and subject to inference queries at any moment.
We additionally propose a new metric to better measure the performance of the continual learning methods subject to inference queries at any moment.
To address the challenging setup and evaluation protocol, we propose an effective method that employs a new memory management scheme and novel learning techniques.
Our empirical validation demonstrates that the proposed method outperforms prior arts by large margins. Code and data splits are available at https://github.com/naver-ai/i-Blurry.
Counterfactual Plans under Distributional Ambiguity
Authors: Duy Nguyen, Viet Anh Nguyen Ngoc Bui
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: 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.
Capturing Structural Locality in Non-parametric Language Models
Authors: Junxian He, Graham Neubig, Vincent Josua Hellendoorn Frank F. Xu
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies. Some examples include topical clusters in text or project hierarchies in source code repositories. In this paper, we explore utilizing this structural locality within non-parametric language models, which generate sequences that reference retrieved examples from an external source. We propose a simple yet effective approach for adding locality information into such models by adding learned parameters that improve the likelihood of retrieving examples from local neighborhoods. Experiments on two different domains, Java source code and Wikipedia text, demonstrate that locality features improve model efficacy over models without access to these features, with interesting differences. We also perform an analysis of how and where locality features contribute to improving performance and why the traditionally used contextual similarity metrics alone are not enough to grasp the locality structure.
Autonomous Reinforcement Learning: Formalism and Benchmarking
Authors: Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn Archit Sharma
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of its resemblance to how humans and animals acquire skills through experience. However, real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world, whereas common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts. This discrepancy presents a major challenge when we attempt to take RL algorithms developed for episodic simulated environments and run  them on real-world platforms, such as robots. In this paper, we aim to address this discrepancy by laying out a framework for Autonomous Reinforcement Learning (ARL): reinforcement learning where the agent not only learns through its own experience, but also contends with lack of human supervision to reset between trials. We introduce a simulated benchmark EARL based on this framework, containing a set of diverse and challenging simulated tasks reflective of the hurdles introduced to learning when only a minimal reliance on extrinsic intervention can be assumed. We show that standard approaches to episodic RL and existing approaches struggle as interventions are minimized, underscoring the need for developing new algorithms for reinforcement learning with a greater focus on autonomy.
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
Authors: Guan-Horng Liu, Evangelos Theodorou Tianrong Chen
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: 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.
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers
Authors: Minghao Zhang, Nicklas Hansen, Huazhe Xu, Xiaolong Wang Ruihan Yang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimensional depth sensor inputs. While learning-based locomotion has made great advances using RL, most methods still rely on domain randomization for training blind agents that generalize to challenging terrains. Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead. In this paper, we introduce LocoTransformer, an end-to-end RL method that leverages both proprioceptive states and visual observations for locomotion control. We evaluate our method in challenging simulated environments with different obstacles and uneven terrain. We transfer our learned policy from simulation to a real robot by running it indoor and in-the-wild with unseen obstacles and terrain. Our method not only significantly improves over baselines, but also achieves far better generalization performance, especially when transferred to the real robot. Our project page with videos is at https://rchalyang.github.io/LocoTransformer/.
A Comparison of Hamming Errors of Representative Variable Selection Methods
Authors: Longlin Wang Tracy Ke
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: Lasso is a celebrated method for variable selection in linear models, but it faces challenges when the covariates are moderately or strongly correlated. This motivates alternative approaches such as using a non-convex penalty, adding a ridge regularization, or conducting a post-Lasso thresholding. In this paper, we compare Lasso with 5 other methods: Elastic net, SCAD, forward selection, thresholded Lasso, and forward backward selection. We measure their performances theoretically by the expected Hamming error, assuming that the regression coefficients are ${\it iid}$ drawn from a two-point mixture and that the Gram matrix is block-wise diagonal. By deriving the rates of convergence of Hamming errors and the phase diagrams, we obtain useful conclusions about the pros and cons of different methods.
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
Authors: Junhao Wen, Christos Davatzikos Zhijian Yang
Published in: International Conference on Learning Representations 2022 (Main Conference Track), 2022

Abstract: A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods don't explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover, unsupervised methods may parse heterogeneity that is driven by nuisance confounding factors that affect brain structure or function, rather than heterogeneity relevant to a pathology of interest. On the other hand, semi-supervised clustering methods seek to derive a dichotomous subtype membership, ignoring the truth that disease heterogeneity spatially and temporally extends along a continuum.  To address the aforementioned limitations, herein, we propose a novel method, termed Surreal-GAN (Semi-SUpeRvised ReprEsentAtion Learning via GAN). Using cross-sectional imaging data, Surreal-GAN dissects underlying disease-related heterogeneity under the principle of semi-supervised clustering (cluster mappings from normal control to patient), proposes a continuously dimensional representation, and infers the disease severity of patients at individual level along each dimension. The model first learns a transformation function from normal control (CN) domain to the patient (PT) domain with latent variables controlling transformation directions. An inverse mapping function together with regularization on function continuity, pattern orthogonality and monotonicity was also imposed to make sure that the transformation function captures necessarily meaningful imaging patterns with clinical significance. We first validated the model through extensive semi-synthetic experiments, and then demonstrate its potential in capturing biologically plausible imaging patterns in Alzheimer's disease (AD).