GenAI4ELab/papercli-papers huggingface dataset:
ManyDG: Many-domain Generalization for Healthcare Applications
Authors: Chaoqi Yang; M Brandon Westover; Jimeng Sun
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The vast amount of health data has been continuously collected for each patient, providing opportunities to support diverse healthcare predictive tasks such as seizure detection and hospitalization prediction. Existing models are mostly trained on other patients’ data and evaluated on new patients. Many of them might suffer from poor generalizability. One key reason can be overfitting due to the unique information related to patient identities and their data collection environments, referred to as patient covariates in the paper. These patient covariates usually do not contribute to predicting the targets but are often difficult to remove. As a result, they can bias the model training process and impede generalization. In healthcare applications, most existing domain generalization methods assume a small number of domains. In this paper, considering the diversity of patient covariates, we propose a new setting by treating each patient as a separate domain (leading to many domains). We develop a new domain generalization method ManyDG, that can scale to such many-domain problems. Our method identifies the patient do- main covariates by mutual reconstruction, and removes them via an orthogonal projection step. Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e.g., 3.7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning. The code is available at https://github.com/ycq091044/ManyDG.
Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance
Authors: Jiaming Qiu; Xiongtao Dai
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We extend metric learning by studying the Riemannian manifold structure of the underlying data space induced by dissimilarity measures between data points. The key quantity of interest here is the Riemannian metric, which characterizes the Riemannian geometry and defines straight lines and derivatives on the manifold. Being able to estimate the Riemannian metric allows us to gain insights into the underlying manifold and compute geometric features such as the geodesic curves. We model the observed dissimilarity measures as noisy responses generated from a function of the intrinsic geodesic distance between data points. A new local regression approach is proposed to learn the Riemannian metric tensor and its derivatives based on a Taylor expansion for the squared geodesic distances. Our framework is general and accommodates different types of responses, whether they are continuous, binary, or comparative, extending the existing works which consider a single type of response at a time. We develop theoretical foundation for our method by deriving the rates of convergence for the asymptotic bias and variance of the estimated metric tensor. The proposed method is shown to be versatile in simulation studies and a real data application involving taxi trip time in New York City.
Analysis of Error Feedback in Compressed Federated Non-Convex Optimization
Authors: Xiaoyun Li; Ping Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Communication cost between the clients and the central server could be a bottleneck in real-world Federated Learning (FL) systems. In classical distributed learning, the method of Error Feedback (EF) has been a popular technique to remedy the downsides of biased gradient compression, but literature on applying EF to FL is still very limited. In this work, we propose a compressed FL scheme equipped with error feedback, named Fed-EF, with two variants depending on the global optimizer. We provide theoretical analysis showing that Fed-EF matches the convergence rate of the full-precision FL counterparts in non-convex optimization under data heterogeneity. Moreover, we initiate the first analysis of EF under partial client participation, which is an important scenario in FL, and demonstrate that the convergence rate of Fed-EF exhibits an extra slow down factor due to the ``stale error compensation'' effect. Experiments are conducted to validate the efficacy of Fed-EF in practical FL tasks and justify our theoretical findings.
Learnable Visual Words for Interpreting Image Recognition Models
Authors: Wenxiao Xiao; Zhengming Ding; Hongfu Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: To interpret deep models' predictions, attention-based visual cues are widely used in addressing *why* deep models make such predictions. Beyond that, the current research community becomes more interested in reasoning *how* deep models make predictions, where some prototype-based methods employ interpretable representations with their corresponding visual cues to reveal the black-box mechanism of deep model behaviors. However, these pioneering attempts only either learn the category-specific prototypes and deteriorate with their generalization ability deterioration or demonstrate several illustrative examples without a quantitative evaluation of visual-based interpretability narrowing their practical usages. In this paper, we revisit the concept of visual words and propose the Learnable Visual Words (LVW) to interpret the model prediction behaviors with two novel modules: semantic visual words learning and dual fidelity preservation. The semantic visual words learning relaxes the category-specific constraint, enabling the generic visual words shared across multiple categories. Beyond employing the visual words for prediction to align visual words with the base model, our dual fidelity preservation also includes the attention-guided semantic alignment that encourages the learned visual words to focus on the same conceptual regions for prediction. Experiments on six visual benchmarks demonstrate the superior effectiveness of our proposed LVW in both accuracy and interpretation fidelity over the state-of-the-art methods. Moreover, we elaborate on various in-depth analyses to further explore the learned visual words and the generalizability of our method for unseen categories.
Measuring axiomatic soundness of counterfactual image models
Authors: Miguel Monteiro; Fabio De Sousa Ribeiro; Nick Pawlowski; Daniel C. Castro; Ben Glocker
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We present a general framework for evaluating image counterfactuals. The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structural causal models. However, their flexibility makes counterfactual identifiability impossible in the general case.
Motivated by these issues, we revisit Pearl's axiomatic definition of counterfactuals to determine the necessary constraints of any counterfactual inference model: composition, reversibility, and effectiveness. We frame counterfactuals as functions of an input variable, its parents, and counterfactual parents and use the axiomatic constraints to restrict the set of functions that could represent the counterfactual, thus deriving distance metrics between the approximate and ideal functions. We demonstrate how these metrics can be used to compare and choose between different approximate counterfactual inference models and to provide insight into a model's shortcomings and trade-offs.
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Timeseries Forecasting and Prediction
Authors: Iman Deznabi; Madalina Fiterau
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: One of the challenges in multivariate time series modeling is that changes in signals occur with different frequencies, even when the sampling rate is consistent across signals. In the case of multivariate time series prediction, the outcome is also determined by patterns of different frequencies. These encapsulate both long-term and short-term effects, which have so far not been sufficiently leveraged by deep learning time series models. We fill this gap by introducing a framework, called MultiWave, which augments any deep learning time series model with components operating at the intrinsic frequencies of the signals. MultiWave applies wavelet decomposition on each signal to obtain subsignals of different frequencies and groups all subsignals in the same frequency band together to train a component. The output of the components is combined through a gating mechanism that removes irrelevant frequencies for the given predictive task. We show that MultiWave accurately determines the informative frequency bands and that the augmented models including components trained to operate on those bands outperform the original models. We further show that applying MultiWave on top of different deep learning models improves their performance in several real-world applications.
Robustness of Unsupervised Representation Learning without Labels
Authors: Aleksandar Petrov; Marta Kwiatkowska
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Unsupervised representation learning leverages large unlabeled datasets and is competitive with supervised learning. But non-robust encoders may affect downstream task robustness. Recently, robust representation encoders have become of interest. Still, all prior work evaluates robustness using a downstream classification task. Instead, we propose a family of unsupervised robustness measures, which are model- and task-agnostic and label-free. We benchmark state-of-the-art representation encoders and show that none dominates the rest. We offer unsupervised extensions to the FGSM and PGD attacks. When used in adversarial training, they improve most unsupervised robustness measures, including certified robustness. We validate our results against a linear probe and show that, for MOCOv2, adversarial training results in 3 times higher certified accuracy, a 2-fold decrease in impersonation attack success rate and considerable improvements in certified robustness.
The Crossword Puzzle: Simplifying Deep Neural Network Pruning with Fabulous Coordinates
Authors: Yicun Duan; Wangkai Jin; Xiangjun Peng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Pruning is a promising technique to shrink the size of Deep Neural Network models with only negligible accuracy overheads. Recent efforts rely on experience-derived metric to guide pruning procedure, which heavily saddles with the effective generalization of pruning methods. We propose The Cross Puzzle, a new method to simplify this procedure by automatically deriving pruning metrics. The key insight behind our method is that: \textit{For Deep Neural Network Models, a Pruning-friendly Distribution of model's weights can be obtained, given a proper Coordinate}. We experimentally confirm the above insight, and denote the new Coordinate as the Fabulous Coordinates. Our quantitative evaluation results show that: the Crossword Puzzle can find a simple yet effective metric, which outperforms the state-of-the-art pruning methods by delivering no accuracy degradation on ResNet-56 (CIFAR-10)/-101 (ImageNet), while the pruning rate is raised to 70\%/50\% for the respective models.
Causal Attention to Exploit Transient Emergence of Causal Effect
Authors: Xiaolei Ru; Xin-Ya Zhang; Jack Murdoch Moore; Gang Yan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a causal reasoning mechanism called $\textit{causal attention}$ that can improve performance of machine learning models on a class of causal inference tasks by revealing the generation process behind the observed data. We consider the problem of reconstructing causal networks (e.g., biological neural networks) connecting large numbers of variables (e.g., nerve cells), of which evolution is governed by nonlinear dynamics consisting of weak coupling-drive (i.e., causal effect) and strong self-drive (dominants the evolution). The core difficulty is sparseness of causal effect that emerges (the coupling force is significant) only momentarily and otherwise remains dormant in the neural activity sequence. $\textit{Causal attention}$ is designed to guide the model to make inference focusing on the critical regions of time series data where causality may manifest. Specifically, attention coefficients are assigned autonomously by a neural network trained to maximise the Attention-extended Transfer Entropy, which is a novel generalization of the iconic transfer entropy metric. Our results show that, without any prior knowledge of dynamics, $\textit{causal attention}$ explicitly identifies areas where the strength of coupling-drive is distinctly greater than zero. This innovation substantially improves reconstruction performance for both synthetic and real causal networks using data generated by neuronal models widely used in neuroscience.
Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning
Authors: Aviral Kumar; Anikait Singh; Frederik Ebert; Yanlai Yang; Chelsea Finn; Sergey Levine
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent progress in deep learning highlights the tremendous potential of utilizing diverse datasets for achieving effective generalization and makes it enticing to consider leveraging broad datasets for attaining more robust generalization in robotic learning as well. However, in practice we likely will want to learn a new skill in a new environment that is unlikely to be contained in the prior data. Therefore we ask: how can we leverage existing diverse offline datasets in combination with small amounts of task-specific data to solve new tasks, while still enjoying the generalization benefits of training on large amounts of data? In this paper, we demonstrate that end-to-end offline RL can be an effective approach for doing this, without the need for any representation learning or vision-based pre-training. We present pre-training for robots (PTR), a framework based on offline RL that attempts to effectively learn new tasks by combining pre-training on existing robotic datasets with rapid fine-tuning on a new task, with as a few as 10 demonstrations. At its core, PTR applies an existing offline RL method such as conservative Q-learning (CQL), but extends it to include several crucial design decisions that enable PTR to actually work and outperform a variety of prior methods. To the best of our knowledge, PTR is the first offline RL method that succeeds at learning new tasks in a new domain on a real WidowX robot with as few as 10 task demonstrations, by effectively leveraging an existing dataset of diverse multi-task robot data collected in a variety of toy kitchens. We present an accompanying overview video at https://www.youtube.com/watch?v=yAWgyLJD5lY&ab_channel=PTRICLR
Adversarially Robust Neural Lyapunov Control
Authors: Li Wei; Yuankun Jiang; Chenglin Li; Wenrui Dai; Junni Zou; Hongkai Xiong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: State-of-the-art learning-based stability control methods for nonlinear robotic systems suffer from the issue of reality gap, which stems from discrepancy of the system dynamics between training and target (test) environments. To mitigate this gap, we propose an adversarially robust neural Lyapunov control (ARNLC) method to improve the robustness and generalization capabilities for Lyapunov theory-based stability control. Specifically, inspired by adversarial learning, we introduce an adversary to simulate the dynamics discrepancy, which is learned through deep reinforcement learning to generate the worst-case perturbations during the controller's training. By alternatively updating the controller to minimize the perturbed Lyapunov risk and the adversary to deviate the controller from its objective, the learned control policy enjoys a theoretical guarantee of stability. Empirical evaluations on five stability control tasks with the uniform and worst-case perturbations demonstrate that ARNLC not only accelerates the convergence to asymptotic stability, but can generalize better in the entire perturbation space.
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Authors: Dexiong Chen; Bowen Fan; Carlos Oliver; Karsten Borgwardt
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences between data instances across the datasets, to a common low-dimensional Euclidean space. Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between instances from two different datasets, while only requiring intra-dataset pairwise dissimilarities as input. This unique characteristic makes our approach applicable to datasets without access to the input features, such as solving the inexact graph matching problem. We propose an alternating optimization scheme to solve the problem that can fully benefit from the optimization techniques for MDS and Wasserstein Procrustes. We demonstrate the effectiveness of our approach in several applications, including joint visualization of two datasets, unsupervised heterogeneous domain adaptation, graph matching, and protein structure alignment. The implementation of our work is available at https://github.com/BorgwardtLab/JointMDS.
Emergence of Maps in the Memories of Blind Navigation Agents
Authors: Erik Wijmans; Manolis Savva; Irfan Essa; Stefan Lee; Ari S. Morcos; Dhruv Batra
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Animal navigation research posits that organisms build and maintain internal spa- tial representations, or maps, of their environment. We ask if machines – specifically, artificial intelligence (AI) navigation agents – also build implicit (or ‘mental’) maps. A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial. Unlike animal navigation, we can judiciously design the agent’s perceptual system and control the learning paradigm to nullify alternative navigation mechanisms. Specifically, we train ‘blind’ agents – with sensing limited to only egomotion and no other sensing of any kind – to perform PointGoal navigation (‘go to $\Delta$x, $\Delta$y’) via reinforcement learning. Our agents are composed of navigation-agnostic components (fully-connected and recurrent neural networks), and our experimental setup provides no inductive bias towards mapping. Despite these harsh conditions, we find that blind agents are (1) surprisingly effective navigators in new environments (∼95% success); (2) they utilize memory over long horizons (remembering ∼1,000 steps of past experience in an episode); (3) this memory enables them to exhibit intelligent behavior (following walls, detecting collisions, taking shortcuts); (4) there is emergence of maps and collision detection neurons in the representations of the environment built by a blind agent as it navigates; and (5) the emergent maps are selective and task dependent (e.g. the agent ‘forgets’ exploratory detours). Overall, this paper presents no new techniques for the AI audience, but a surprising finding, an insight, and an explanation.
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Authors: Marzi Heidari; Abdullah Alchihabi; Qing En; Yuhong Guo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test tasks. 
In this paper, we develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot classification. 
Different from existing prototypical few-shot methods that use the averages of support instances to calculate the class prototypes, we propose to learn class prototypes from the concatenated features of the support set in a parametric fashion and meta-learn the model by enforcing prototype-based regularization on the query set. 
In addition, we fine-tune the model in the target domain in a transductive manner using a weighted-moving-average self-training approach on the query instances. 
We conduct experiments on multiple cross-domain few-shot benchmark datasets. 
The empirical results demonstrate that APPL yields superior performance than many state-of-the-art methods.
Parameterized projected Bellman operator
Authors: Théo Vincent; Alberto Maria Metelli; Jan Peters; Marcello Restelli; Carlo D'Eramo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The Bellman operator is a cornerstone of reinforcement learning, widely used in a plethora of works, from value-based methods to modern actor-critic approaches. In problems with unknown models, the Bellman operator requires transition samples that strongly determine its behavior, as uninformative samples can result in negligible updates or long detours before reaching the fixed point. In this work, we introduce the novel idea of obtaining an approximation of the Bellman operator, which we call projected Bellman operator (PBO). Our PBO is a parametric operator on the parameter space of a given value function. Given the parameters of a value function, PBO outputs the parameters of a new value function and converges to a fixed point in the limit, as a standard Bellman operator. Notably, our PBO can approximate repeated applications of the true Bellman operator at once, as opposed to the sequential nature of the standard Bellman operator. We prove the important consequences of this finding for different classes of problems by analyzing PBO in terms of stability, convergence, and approximation error. Eventually, we propose an approximate value-iteration algorithm to show how PBO can overcome the limitations of classical methods, opening up multiple research directions as a novel paradigm in reinforcement learning.
A Differential Geometric View and Explainability of GNN on Evolving Graphs
Authors: Yazheng Liu; Xi Zhang; Sihong Xie
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction. Graphs can be evolving and it is vital to formally model and understand how a trained GNN responds to graph evolution. We propose a smooth parameterization of the GNN predicted distributions using axiomatic attribution, where the distributions are on a low dimensional manifold within a high-dimensional embedding space. We exploit the differential geometric viewpoint to model distributional evolution as smooth curves on the manifold. We reparameterize families of curves on the manifold and design a convex optimization problem to find a unique curve that concisely approximates the distributional evolution for human interpretation. Extensive experiments on node classification, link prediction, and graph classification tasks with evolving graphs demonstrate the better sparsity, faithfulness, and intuitiveness of the proposed method over the state-of-the-art methods.