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Title: One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement Abstract: Multi-label learning (MLL) learns from the examples each associated with multiple labels simultaneously, where the high cost of annotating all relevant labels for each training example is challenging for real-... |
Title: Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning Abstract: In deep reinforcement learning (RL), data augmentation is widely considered as a tool to induce a set of useful priors about semantic consistency and improve sample efficiency and generalization performance. However, even when th... |
Title: Deep Learning Opacity in Scientific Discovery Abstract: Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossibl... |
Title: Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data Abstract: Society needs to develop a system to detect hate and offense to build a healthy and safe environment. However, current research in this field still faces four major shortcomings, including deficient pre-processing... |
Title: Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top Abstract: Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in collaborative and federated learning. However, many fruitful directions, s... |
Title: FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks Abstract: Algorithmic decision making driven by neural networks has become very prominent in applications that directly affect people's quality of life. In this paper, we study the problem of verifying, training, and guara... |
Title: A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs Abstract: We consider online learning with feedback graphs, a sequential decision-making framework where the learner's feedback is determined by a directed graph over the action set. We present a computationally efficient algor... |
Title: RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring Abstract: In financial credit scoring, loan applications may be approved or rejected. We can only observe default/non-default labels for approved samples but have no observations for rejected samples, whi... |
Title: Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks Abstract: To accelerate the training of graph convolutional networks (GCNs), many sampling-based methods have been developed for approximating the embedding aggregation. Among them, a layer-wise approach recursively performs importance sam... |
Title: Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts Abstract: There is a rising interest in industrial online applications where data becomes available sequentially. Inspired by the recommendation of playlists to users where their preferences can be collected during the l... |
Title: Higher-Order Attention Networks Abstract: This paper introduces higher-order attention networks (HOANs), a novel class of attention-based neural networks defined on a generalized higher-order domain called a combinatorial complex (CC). Similar to hypergraphs, CCs admit arbitrary set-like relations between a coll... |
Title: On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models Abstract: The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving. While much effort is put into improving the ML models and learning... |
Title: Graph Machine Learning for Design of High-Octane Fuels Abstract: Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octa... |
Title: Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training Abstract: In this paper, we introduce Cross-View Language Modeling, a simple and effective language model pre-training framework that unifies cross-lingual cross-modal pre-training with shared architectures and objectives. Our a... |
Title: Computing the Variance of Shuffling Stochastic Gradient Algorithms via Power Spectral Density Analysis Abstract: When solving finite-sum minimization problems, two common alternatives to stochastic gradient descent (SGD) with theoretical benefits are random reshuffling (SGD-RR) and shuffle-once (SGD-SO), in whic... |
Title: Graph Neural Networks with Precomputed Node Features Abstract: Most Graph Neural Networks (GNNs) cannot distinguish some graphs or indeed some pairs of nodes within a graph. This makes it impossible to solve certain classification tasks. However, adding additional node features to these models can resolve this p... |
Title: A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin Abstract: Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculati... |
Title: Differentiable programming for functional connectomics Abstract: Mapping the functional connectome has the potential to uncover key insights into brain organisation. However, existing workflows for functional connectomics are limited in their adaptability to new data, and principled workflow design is a challeng... |
Title: Learning-Augmented Algorithms for Online TSP on the Line Abstract: We study the online Traveling Salesman Problem (TSP) on the line augmented with machine-learned predictions. In the classical problem, there is a stream of requests released over time along the real line. The goal is to minimize the makespan of t... |
Title: Hopular: Modern Hopfield Networks for Tabular Data Abstract: While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data. For tabular data, Support Vector Machines (SVMs), Random Forests, and Gradient Boosting are the ... |
Title: How Biased is Your Feature?: Computing Fairness Influence Functions with Global Sensitivity Analysis Abstract: Fairness in machine learning has attained significant focus due to the widespread application of machine learning in high-stake decision-making tasks. Unless regulated with a fairness objective, machine... |
Title: Learning to Untangle Genome Assembly with Graph Convolutional Networks Abstract: A quest to determine the complete sequence of a human DNA from telomere to telomere started three decades ago and was finally completed in 2021. This accomplishment was a result of a tremendous effort of numerous experts who enginee... |
Title: Bayesian Learning to Discover Mathematical Operations in Governing Equations of Dynamic Systems Abstract: Discovering governing equations from data is critical for diverse scientific disciplines as they can provide insights into the underlying phenomenon of dynamic systems. This work presents a new representatio... |
Title: Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts Abstract: Forecasting tasks surrounding the dynamics of low-level human behavior are of significance to multiple research domains. In such settings, methods for explaining specific foreca... |
Title: Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data Abstract: Federated learning (FL) is a privacy-promoting framework that enables potentially large number of clients to collaboratively train machine learning models. In a FL system, a server coordinates the collaboration by col... |
Title: Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction Abstract: In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by their context. Inspired by classical mo... |
Title: Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL Abstract: We introduce an offline reinforcement learning (RL) algorithm that explicitly clones a behavior policy to constrain value learning. In offline RL, it is often important to prevent a policy from selecting unobserved actions... |
Title: RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model Abstract: Counterfactual (CF) explanations for machine learning (ML) models are preferred by end-users, as they explain the predictions of ML models by providing a recourse case to individuals who are adversely impacted by predict... |
Title: Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search Abstract: Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptiv... |
Title: Split-kl and PAC-Bayes-split-kl Inequalities Abstract: We present a new concentration of measure inequality for sums of independent bounded random variables, which we name a split-kl inequality. The inequality combines the combinatorial power of the kl inequality with ability to exploit low variance. While for B... |
Title: Collaborative Learning of Distributions under Heterogeneity and Communication Constraints Abstract: In modern machine learning, users often have to collaborate to learn the distribution of the data. Communication can be a significant bottleneck. Prior work has studied homogeneous users -- i.e., whose data follow... |
Title: Learning to Solve PDE-constrained Inverse Problems with Graph Networks Abstract: Learned graph neural networks (GNNs) have recently been established as fast and accurate alternatives for principled solvers in simulating the dynamics of physical systems. In many application domains across science and engineering,... |
Title: Dataset Distillation using Neural Feature Regression Abstract: Dataset distillation aims to learn a small synthetic dataset that preserves most of the information from the original dataset. Dataset distillation can be formulated as a bi-level meta-learning problem where the outer loop optimizes the meta-dataset ... |
Title: (Machine) Learning What Policies Value Abstract: When a policy prioritizes one person over another, is it because they benefit more, or because they are preferred? This paper develops a method to uncover the values consistent with observed allocation decisions. We use machine learning methods to estimate how muc... |
Title: The Phenomenon of Policy Churn Abstract: We identify and study the phenomenon of policy churn, that is, the rapid change of the greedy policy in value-based reinforcement learning. Policy churn operates at a surprisingly rapid pace, changing the greedy action in a large fraction of states within a handful of lea... |
Title: Cascaded Video Generation for Videos In-the-Wild Abstract: Videos can be created by first outlining a global view of the scene and then adding local details. Inspired by this idea we propose a cascaded model for video generation which follows a coarse to fine approach. First our model generates a low resolution ... |
Title: Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data Abstract: We consider the problem of a Parameter Server (PS) that wishes to learn a model that fits data distributed on the nodes of a graph. We focus on Federated Learning (FL) as a canonical application. One of the main ch... |
Title: Composition of Relational Features with an Application to Explaining Black-Box Predictors Abstract: Relational machine learning programs like those developed in Inductive Logic Programming (ILP) offer several advantages: (1) The ability to model complex relationships amongst data instances; (2) The use of domain... |
Title: Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization Abstract: Adaptive algorithms like AdaGrad and AMSGrad are successful in nonconvex optimization owing to their parameter-agnostic ability -- requiring no a priori knowledge about problem-specific parameters nor tuning of learning ... |
Title: A Log-Linear Time Sequential Optimal Calibration Algorithm for Quantized Isotonic L2 Regression Abstract: We study the sequential calibration of estimations in a quantized isotonic L2 regression setting. We start by showing that the optimal calibrated quantized estimations can be acquired from the traditional is... |
Title: Residual Multiplicative Filter Networks for Multiscale Reconstruction Abstract: Coordinate networks like Multiplicative Filter Networks (MFNs) and BACON offer some control over the frequency spectrum used to represent continuous signals such as images or 3D volumes. Yet, they are not readily applicable to proble... |
Title: SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networks Abstract: Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems via harnessing solar energy available on building envelopes. While methods to assess sola... |
Title: Merlin-Arthur Classifiers: Formal Interpretability with Interactive Black Boxes Abstract: We present a new theoretical framework for making black box classifiers such as Neural Networks interpretable, basing our work on clear assumptions and guarantees. In our setting, which is inspired by the Merlin-Arthur prot... |
Title: On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting Abstract: The availability of large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a training-from-scratch to a fine-tuning paradigm. While in s... |
Title: Defense Against Gradient Leakage Attacks via Learning to Obscure Data Abstract: Federated learning is considered as an effective privacy-preserving learning mechanism that separates the client's data and model training process. However, federated learning is still under the risk of privacy leakage because of the... |
Title: On the reversibility of adversarial attacks Abstract: Adversarial attacks modify images with perturbations that change the prediction of classifiers. These modified images, known as adversarial examples, expose the vulnerabilities of deep neural network classifiers. In this paper, we investigate the predictabili... |
Title: Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora Abstract: As the amount and variety of energetics research increases, machine aware topic identification is necessary to streamline future research pipelines. The makeup of an automatic topi... |
Title: Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data Abstract: Recent medical image reconstruction techniques focus on generating high-quality medical images suitable for clinical use at the lowest possible cost and with the fewest possible adverse effects on patie... |
Title: Core-periphery Models for Hypergraphs Abstract: We introduce a random hypergraph model for core-periphery structure. By leveraging our model's sufficient statistics, we develop a novel statistical inference algorithm that is able to scale to large hypergraphs with runtime that is practically linear wrt. the numb... |
Title: Neural Decoding with Optimization of Node Activations Abstract: The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered. It is shown that the neural decoder can be improved with two novel loss terms on the node's activations. The first loss term imposes a sparse c... |
Title: On the Generalization of Neural Combinatorial Optimization Heuristics Abstract: Neural Combinatorial Optimization approaches have recently leveraged the expressiveness and flexibility of deep neural networks to learn efficient heuristics for hard Combinatorial Optimization (CO) problems. However, most of the cur... |
Title: Sequential Bayesian Neural Subnetwork Ensembles Abstract: Deep neural network ensembles that appeal to model diversity have been used successfully to improve predictive performance and model robustness in several applications. Whereas, it has recently been shown that sparse subnetworks of dense models can match ... |
Title: Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning Abstract: The $Q$-learning algorithm is a simple and widely-used stochastic approximation scheme for reinforcement learning, but the basic protocol can exhibit instability in conjunction with function approximation. Such instability... |
Title: Federated Learning under Distributed Concept Drift Abstract: Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for FL, because drifts arise staggered in time and space (across clients). ... |
Title: Indeterminacy in Latent Variable Models: Characterization and Strong Identifiability Abstract: Most modern latent variable and probabilistic generative models, such as the variational autoencoder (VAE), have certain indeterminacies that are unresolvable even with an infinite amount of data. Recent applications o... |
Title: Learning code summarization from a small and local dataset Abstract: Foundation models (e.g., CodeBERT, GraphCodeBERT, CodeT5) work well for many software engineering tasks. These models are pre-trained (using self-supervision) with billions of code tokens, and then fine-tuned with hundreds of thousands of label... |
Title: Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings Abstract: The classical machine learning paradigm requires the aggregation of user data in a central location where machine learning practitioners can preprocess data, calculate features, tune models and ... |
Title: Offline Reinforcement Learning with Differential Privacy Abstract: The offline reinforcement learning (RL) problem is often motivated by the need to learn data-driven decision policies in financial, legal and healthcare applications. However, the learned policy could retain sensitive information of individuals i... |
Title: NIPQ: Noise Injection Pseudo Quantization for Automated DNN Optimization Abstract: The optimization of neural networks in terms of computation cost and memory footprint is crucial for their practical deployment on edge devices. In this work, we propose a novel quantization-aware training (QAT) scheme called nois... |
Title: BayesFormer: Transformer with Uncertainty Estimation Abstract: Transformer has become ubiquitous due to its dominant performance in various NLP and image processing tasks. However, it lacks understanding of how to generate mathematically grounded uncertainty estimates for transformer architectures. Models equipp... |
Title: Progressive Purification for Instance-Dependent Partial Label Learning Abstract: Partial label learning (PLL) aims to train multi-class classifiers from instances with partial labels (PLs)-a PL for an instance is a set of candidate labels where a fixed but unknown candidate is the true label. In the last few yea... |
Title: Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm Regularizations Abstract: Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied to ... |
Title: Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates Abstract: Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive. Here we show how a multiplicative cyclic learning rate schedule can be used to construct a tradeoff curve in a single trai... |
Title: Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm Abstract: Natural actor-critic (NAC) and its variants, equipped with the representation power of neural networks, have demonstrated impressive empirical success in solving Markov decision problems with large state spaces. In this p... |
Title: DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks Abstract: Efficient deep neural network (DNN) models equipped with compact operators (e.g., depthwise convolutions) have shown great potential in reducing DNNs' theoretical complexity (e.g., the total n... |
Title: Hyperspherical Consistency Regularization Abstract: Recent advances in contrastive learning have enlightened diverse applications across various semi-supervised fields. Jointly training supervised learning and unsupervised learning with a shared feature encoder becomes a common scheme. Though it benefits from ta... |
Title: Faster Rates of Convergence to Stationary Points in Differentially Private Optimization Abstract: We study the problem of approximating stationary points of Lipschitz and smooth functions under $(\varepsilon,\delta)$-differential privacy (DP) in both the finite-sum and stochastic settings. A point $\widehat{w}$ ... |
Title: Dynamic MRI using Learned Transform-based Deep Tensor Low-Rank Network (DTLR-Net) Abstract: While low-rank matrix prior has been exploited in dynamic MR image reconstruction and has obtained satisfying performance, low-rank tensors models have recently emerged as powerful alternative representations for three-di... |
Title: Masked Bayesian Neural Networks : Computation and Optimality Abstract: As data size and computing power increase, the architectures of deep neural networks (DNNs) have been getting more complex and huge, and thus there is a growing need to simplify such complex and huge DNNs. In this paper, we propose a novel sp... |
Title: Bayesian Inference of Stochastic Dynamical Networks Abstract: Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies a... |
Title: Self-Consistency of the Fokker-Planck Equation Abstract: The Fokker-Planck equation (FPE) is the partial differential equation that governs the density evolution of the It\^o process and is of great importance to the literature of statistical physics and machine learning. The FPE can be regarded as a continuity ... |
Title: Dynamic Structure Estimation from Bandit Feedback Abstract: This work present novel method for structure estimation of an underlying dynamical system. We tackle problems of estimating dynamic structure from bandit feedback contaminated by sub-Gaussian noise. In particular, we focus on periodically behaved discre... |
Title: Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs Abstract: This study considers online learning with general directed feedback graphs. For this problem, we present best-of-both-worlds algorithms that achieve nearly tight regret bounds for adversarial environments as well as ... |
Title: Coordinated Double Machine Learning Abstract: Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with high-dimensional covariates, under the assumption of a partially linear model. The ide... |
Title: Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards Abstract: Despite the successful demonstration of autonomous vehicles (AVs), such as self-driving cars, ensuring AV safety remains a challenging task. Although some actors influence an AV's driving decisions more than others, curr... |
Title: Leveraging Systematic Knowledge of 2D Transformations Abstract: The existing deep learning models suffer from out-of-distribution (o.o.d.) performance drop in computer vision tasks. In comparison, humans have a remarkable ability to interpret images, even if the scenes in the images are rare, thanks to the syste... |
Title: Mask-Guided Divergence Loss Improves the Generalization and Robustness of Deep Neural Network Abstract: Deep neural network (DNN) with dropout can be regarded as an ensemble model consisting of lots of sub-DNNs (i.e., an ensemble sub-DNN where the sub-DNN is the remaining part of the DNN after dropout), and thro... |
Title: Federated Learning with a Sampling Algorithm under Isoperimetry Abstract: Federated learning uses a set of techniques to efficiently distribute the training of a machine learning algorithm across several devices, who own the training data. These techniques critically rely on reducing the communication cost -- th... |
Title: DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps Abstract: Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they generally need hundreds or tho... |
Title: Generating Sparse Counterfactual Explanations For Multivariate Time Series Abstract: Since neural networks play an increasingly important role in critical sectors, explaining network predictions has become a key research topic. Counterfactual explanations can help to understand why classifier models decide for p... |
Title: Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs Abstract: The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article ... |
Title: Improving Diffusion Models for Inverse Problems using Manifold Constraints Abstract: Recently, diffusion models have been used to solve various inverse problems in an unsupervised manner with appropriate modifications to the sampling process. However, the current solvers, which recursively apply a reverse diffus... |
Title: Feature Space Particle Inference for Neural Network Ensembles Abstract: Ensembles of deep neural networks demonstrate improved performance over single models. For enhancing the diversity of ensemble members while keeping their performance, particle-based inference methods offer a promising approach from a Bayesi... |
Title: Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection Abstract: We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-lingual transfer learning for zero-shot ... |
Title: Primal-dual extrapolation methods for monotone inclusions under local Lipschitz continuity with applications to variational inequality, conic constrained saddle point, and convex conic optimization problems Abstract: In this paper we consider a class of structured monotone inclusion (MI) problems that consist of... |
Title: Graph Kernels Based on Multi-scale Graph Embeddings Abstract: Graph kernels are conventional methods for computing graph similarities. However, most of the R-convolution graph kernels face two challenges: 1) They cannot compare graphs at multiple different scales, and 2) they do not consider the distributions of... |
Title: On the Effectiveness of Knowledge Graph Embeddings: a Rule Mining Approach Abstract: We study the effectiveness of Knowledge Graph Embeddings (KGE) for knowledge graph (KG) completion with rule mining. More specifically, we mine rules from KGs before and after they have been completed by a KGE to compare possibl... |
Title: Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks Abstract: This paper introduces a new loss function, OSM (One-Sided Margin), to solve maximum-margin classification problems effectively. Unlike the hinge loss, in OSM the margin is explicitly determined with corresponding hyp... |
Title: Shortest Path Networks for Graph Property Prediction Abstract: Most graph neural network models rely on a particular message passing paradigm, where the idea is to iteratively propagate node representations of a graph to each node in the direct neighborhood. While very prominent, this paradigm leads to informati... |
Title: Approximate Network Motif Mining Via Graph Learning Abstract: Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets. However, the high computational complexity of identifying motif sets in arbitrary datasets (motif mining) has limited their use in... |
Title: Policy Gradient Algorithms with Monte-Carlo Tree Search for Non-Markov Decision Processes Abstract: Policy gradient (PG) is a reinforcement learning (RL) approach that optimizes a parameterized policy model for an expected return using gradient ascent. Given a well-parameterized policy model, such as a neural ne... |
Title: Score-Based Generative Models Detect Manifolds Abstract: Score-based generative models (SGMs) need to approximate the scores $\nabla \log p_t$ of the intermediate distributions as well as the final distribution $p_T$ of the forward process. The theoretical underpinnings of the effects of these approximations are... |
Title: Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization Abstract: Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention in many areas, especially in the user growth area which concerns many int... |
Title: Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions Abstract: We analyze the dynamics of large batch stochastic gradient descent with momentum (SGD+M) on the least squares problem when both the number of samples and dimensions are large. In this setting, we show that the d... |
Title: DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis Abstract: Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to... |
Title: Practical Adversarial Multivalid Conformal Prediction Abstract: We give a simple, generic conformal prediction method for sequential prediction that achieves target empirical coverage guarantees against adversarially chosen data. It is computationally lightweight -- comparable to split conformal prediction -- bu... |
Title: Deep Transformer Q-Networks for Partially Observable Reinforcement Learning Abstract: Real-world reinforcement learning tasks often involve some form of partial observability where the observations only give a partial or noisy view of the true state of the world. Such tasks typically require some form of memory,... |
Title: When does return-conditioned supervised learning work for offline reinforcement learning? Abstract: Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL). RCSL algorithms learn the distri... |
Title: Revisiting the General Identifiability Problem Abstract: We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We show t... |
Title: Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning Abstract: Most theoretically motivated work in the offline reinforcement learning setting requires precise uncertainty estimates. This requirement restricts the algorithms derived in that work to the tabular and linear settings... |
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