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Title: Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements Abstract: With the recent proliferation of artificial intelligence systems, there has been a surge in the demand for explainability of these systems. Explanations help to redu... |
Title: Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem? Abstract: We propose a geometric scattering-based graph neural network (GNN) for approximating solutions of the NP-hard maximal clique (MC) problem. We construct a loss function with two terms, one which encourages the network to fin... |
Title: Causality Learning With Wasserstein Generative Adversarial Networks Abstract: Conventional methods for causal structure learning from data face significant challenges due to combinatorial search space. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constrain... |
Title: Constraining Gaussian processes for physics-informed acoustic emission mapping Abstract: The automated localisation of damage in structures is a challenging but critical ingredient in the path towards predictive or condition-based maintenance of high value structures. The use of acoustic emission time of arrival... |
Title: Transferring Studies Across Embodiments: A Case Study in Confusion Detection Abstract: Human-robot studies are expensive to conduct and difficult to control, and as such researchers sometimes turn to human-avatar interaction in the hope of faster and cheaper data collection that can be transferred to the robot d... |
Title: GINK: Graph-based Interaction-aware Kinodynamic Planning via Reinforcement Learning for Autonomous Driving Abstract: There are many challenges in applying deep reinforcement learning (DRL) to autonomous driving in a structured environment such as an urban area. This is because the massive traffic flows moving al... |
Title: Finding Rule-Interpretable Non-Negative Data Representation Abstract: Non-negative Matrix Factorization (NMF) is an intensively used technique for obtaining parts-based, lower dimensional and non-negative representation of non-negative data. It is a popular method in different research fields. Scientists perform... |
Title: Functional Connectivity Methods for EEG-based Biometrics on a Large, Heterogeneous Dataset Abstract: This study examines the utility of functional connectivity (FC) and graph-based (GB) measures with a support vector machine classifier for use in electroencephalogram (EEG) based biometrics. Although FC-based fea... |
Title: Offline Reinforcement Learning with Causal Structured World Models Abstract: Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment. Previous model-based offline RL methods learn fully ... |
Title: Evaluating Transfer-based Targeted Adversarial Perturbations against Real-World Computer Vision Systems based on Human Judgments Abstract: Computer vision systems are remarkably vulnerable to adversarial perturbations. Transfer-based adversarial images are generated on one (source) system and used to attack anot... |
Title: Zero-Shot Bird Species Recognition by Learning from Field Guides Abstract: We exploit field guides to learn bird species recognition, in particular zero-shot recognition of unseen species. The illustrations contained in field guides deliberately focus on discriminative properties of a species, and can serve as s... |
Title: PAC Statistical Model Checking of Mean Payoff in Discrete- and Continuous-Time MDP Abstract: Markov decision processes (MDP) and continuous-time MDP (CTMDP) are the fundamental models for non-deterministic systems with probabilistic uncertainty. Mean payoff (a.k.a. long-run average reward) is one of the most cla... |
Title: Safety Certification for Stochastic Systems via Neural Barrier Functions Abstract: Providing non-trivial certificates of safety for non-linear stochastic systems is an important open problem that limits the wider adoption of autonomous systems in safety-critical applications. One promising solution to address th... |
Title: Indirect Active Learning Abstract: Traditional models of active learning assume a learner can directly manipulate or query a covariate $X$ in order to study its relationship with a response $Y$. However, if $X$ is a feature of a complex system, it may be possible only to indirectly influence $X$ by manipulating ... |
Title: Orthogonal Transform based Generative Adversarial Network for Image Dehazing Abstract: Image dehazing has become one of the crucial preprocessing steps for any computer vision task. Most of the dehazing methods try to estimate the transmission map along with the atmospheric light to get the dehazed image in the ... |
Title: XPASC: Measuring Generalization in Weak Supervision Abstract: Weak supervision is leveraged in a wide range of domains and tasks due to its ability to create massive amounts of labeled data, requiring only little manual effort. Standard approaches use labeling functions to specify signals that are relevant for t... |
Title: On the duality between contrastive and non-contrastive self-supervised learning Abstract: Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches. While differen... |
Title: Modeling electronic health record data using a knowledge-graph-embedded topic model Abstract: The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has be... |
Title: Automated visual inspection of silicon detectors in CMS experiment Abstract: In the CMS experiment at CERN, Geneva, a large number of HGCAL sensor modules are fabricated in advanced laboratories around the world. Each sensor module contains about 700 checkpoints for visual inspection thus making it almost imposs... |
Title: Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics Abstract: This paper introduces algorithms to select/design kernels in Gaussian process regression/kriging surrogate modeling techniques. We adopt the setting of kernel method solutions in ad hoc functional spaces,... |
Title: On the Generalization of Wasserstein Robust Federated Learning Abstract: In federated learning, participating clients typically possess non-i.i.d. data, posing a significant challenge to generalization to unseen distributions. To address this, we propose a Wasserstein distributionally robust optimization scheme ... |
Title: Rate-Optimal Online Convex Optimization in Adaptive Linear Control Abstract: We consider the problem of controlling an unknown linear dynamical system under adversarially changing convex costs and full feedback of both the state and cost function. We present the first computationally-efficient algorithm that att... |
Title: Impact of the composition of feature extraction and class sampling in medicare fraud detection Abstract: With healthcare being critical aspect, health insurance has become an important scheme in minimizing medical expenses. Following this, the healthcare industry has seen a significant increase in fraudulent act... |
Title: Fair Classification via Transformer Neural Networks: Case Study of an Educational Domain Abstract: Educational technologies nowadays increasingly use data and Machine Learning (ML) models. This gives the students, instructors, and administrators support and insights for the optimum policy. However, it is well ac... |
Title: Hybrid Models for Mixed Variables in Bayesian Optimization Abstract: We systematically describe the problem of simultaneous surrogate modeling of mixed variables (i.e., continuous, integer and categorical variables) in the Bayesian optimization (BO) context. We provide a unified hybrid model using both Monte-Car... |
Title: MetaLR: Layer-wise Learning Rate based on Meta-Learning for Adaptively Fine-tuning Medical Pre-trained Models Abstract: When applying transfer learning for medical image analysis, downstream tasks often have significant gaps with the pre-training tasks. Previous methods mainly focus on improving the transferabil... |
Title: Generalization for multiclass classification with overparameterized linear models Abstract: Via an overparameterized linear model with Gaussian features, we provide conditions for good generalization for multiclass classification of minimum-norm interpolating solutions in an asymptotic setting where both the num... |
Title: A Deep Reinforcement Learning Framework For Column Generation Abstract: Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns). CG is the workhorse for tackling large-scale integer linear programs, which rely on CG to solve LP rela... |
Title: Instant Graph Neural Networks for Dynamic Graphs Abstract: Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with mi... |
Title: Regularization-wise double descent: Why it occurs and how to eliminate it Abstract: The risk of overparameterized models, in particular deep neural networks, is often double-descent shaped as a function of the model size. Recently, it was shown that the risk as a function of the early-stopping time can also be d... |
Title: Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission Abstract: This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a ... |
Title: Slot Order Matters for Compositional Scene Understanding Abstract: Empowering agents with a compositional understanding of their environment is a promising next step toward solving long-horizon planning problems. On the one hand, we have seen encouraging progress on variational inference algorithms for obtaining... |
Title: Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation Abstract: Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on... |
Title: Adversarial Unlearning: Reducing Confidence Along Adversarial Directions Abstract: Supervised learning methods trained with maximum likelihood objectives often overfit on training data. Most regularizers that prevent overfitting look to increase confidence on additional examples (e.g., data augmentation, adversa... |
Title: Supernet Training for Federated Image Classification under System Heterogeneity Abstract: Efficient deployment of deep neural networks across many devices and resource constraints, especially on edge devices, is one of the most challenging problems in the presence of data-privacy preservation issues. Conventiona... |
Title: Infinite Recommendation Networks: A Data-Centric Approach Abstract: We leverage the Neural Tangent Kernel and its equivalence to training infinitely-wide neural networks to devise $\infty$-AE: an autoencoder with infinitely-wide bottleneck layers. The outcome is a highly expressive yet simplistic recommendation ... |
Title: On the Privacy Properties of GAN-generated Samples Abstract: The privacy implications of generative adversarial networks (GANs) are a topic of great interest, leading to several recent algorithms for training GANs with privacy guarantees. By drawing connections to the generalization properties of GANs, we prove ... |
Title: Towards Group Learning: Distributed Weighting of Experts Abstract: Aggregating signals from a collection of noisy sources is a fundamental problem in many domains including crowd-sourcing, multi-agent planning, sensor networks, signal processing, voting, ensemble learning, and federated learning. The core questi... |
Title: MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data Abstract: Numerical reasoning over hybrid data containing both textual and tabular content (e.g., financial reports) has recently attracted much attention in the NLP community. However, existing question answering (QA) benchmarks o... |
Title: Detecting Pulmonary Embolism from Computed Tomography Using Convolutional Neural Network Abstract: The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vasc... |
Title: HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning Abstract: The use of machine learning (ML) models in decision-making contexts, particularly those used in high-stakes decision-making, are fraught with issue and peril since a person - not a machine - must ultimately be held accountable for th... |
Title: Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning Abstract: While the empirical success of self-supervised learning (SSL) heavily relies on the usage of deep nonlinear models, many theoretical works proposed to understand SSL still focus on linear ones. In this paper, we study t... |
Title: Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control Abstract: Machine-learned black-box policies are ubiquitous for nonlinear control problems. Meanwhile, crude model information is often available for these problems from, e.g., linear approximations of nonlinear dynamics. We study ... |
Title: Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code Abstract: Few-shot learning with large-scale, pre-trained language models is a powerful way to answer questions about code, e.g., how to complete a given code example, or even generate code snippets from scratch. Th... |
Title: Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information Abstract: There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been focused on human sensing applic... |
Title: SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG Abstract: Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with millisecond resolution, rendering it a viable method in neuroscience and healthcare. However, its utility is limited... |
Title: Sample-Efficient Reinforcement Learning of Partially Observable Markov Games Abstract: This paper considers the challenging tasks of Multi-Agent Reinforcement Learning (MARL) under partial observability, where each agent only sees her own individual observations and actions that reveal incomplete information abo... |
Title: A New Security Boundary of Component Differentially Challenged XOR PUFs Against Machine Learning Modeling Attacks Abstract: Physical Unclonable Functions (PUFs) are promising security primitives for resource-constrained network nodes. The XOR Arbiter PUF (XOR PUF or XPUF) is an intensively studied PUF invented t... |
Title: Learning Soft Constraints From Constrained Expert Demonstrations Abstract: Inverse reinforcement learning (IRL) methods assume that the expert data is generated by an agent optimizing some reward function. However, in many settings, the agent may optimize a reward function subject to some constraints, where the ... |
Title: Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling Abstract: Restricted Boltzmann Machines are simple and powerful generative models capable of encoding any complex dataset. Despite all their advantages, in practice, trainings are often unstable, and it is hard to assess their quality beca... |
Title: Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees Abstract: Communication compression is a crucial technique for modern distributed learning systems to alleviate their communication bottlenecks over slower networks. Despite recent intensive studies of gradient compressio... |
Title: PNODE: A memory-efficient neural ODE framework based on high-level adjoint differentiation Abstract: Neural ordinary differential equations (neural ODEs) have emerged as a novel network architecture that bridges dynamical systems and deep learning. However, the gradient obtained with the continuous adjoint metho... |
Title: Rashomon Capacity: A Metric for Predictive Multiplicity in Probabilistic Classification Abstract: Predictive multiplicity occurs when classification models with nearly indistinguishable average performances assign conflicting predictions to individual samples. When used for decision-making in applications of con... |
Title: Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards Abstract: Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms. Th... |
Title: Decentralized Training of Foundation Models in Heterogeneous Environments Abstract: Training foundation models, such as GPT-3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months. These models are typically trained in specialized clusters featuring fast,... |
Title: Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection Abstract: It is known that Deep Neural networks (DNNs) are vulnerable to adversarial attacks, and the adversarial robustness of DNNs could be improved by adding adversarial noises to training data... |
Title: Two Ways of Understanding Social Dynamics: Analyzing the Predictability of Emergence of Objects in Reddit r/place Dependent on Locality in Space and Time Abstract: Lately, studying social dynamics in interacting agents has been boosted by the power of computer models, which bring the richness of qualitative work... |
Title: Sequential Permutation Testing of Random Forest Variable Importance Measures Abstract: Hypothesis testing of random forest (RF) variable importance measures (VIMP) remains the subject of ongoing research. Among recent developments, heuristic approaches to parametric testing have been proposed whose distributiona... |
Title: Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks Abstract: A striking observation about iterative magnitude pruning (IMP; Frankle et al. 2020) is that $\unicode{x2014}$ after just a few hundred steps of dense training $\unicode{x2014}$ the method can find a sparse sub-networ... |
Title: Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares Abstract: Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails has links to the generalization error. While these studies have shed light on interesting aspects of th... |
Title: Exponential Separations in Symmetric Neural Networks Abstract: In this work we demonstrate a novel separation between symmetric neural network architectures. Specifically, we consider the Relational Network~\parencite{santoro2017simple} architecture as a natural generalization of the DeepSets~\parencite{zaheer20... |
Title: Deep Learning Architecture Based Approach For 2D-Simulation of Microwave Plasma Interaction Abstract: This paper presents a convolutional neural network (CNN)-based deep learning model, inspired from UNet with series of encoder and decoder units with skip connections, for the simulation of microwave-plasma inter... |
Title: Entangled Residual Mappings Abstract: Residual mappings have been shown to perform representation learning in the first layers and iterative feature refinement in higher layers. This interplay, combined with their stabilizing effect on the gradient norms, enables them to train very deep networks. In this paper, ... |
Title: Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions Abstract: High-dimensional Partial Differential Equations (PDEs) are a popular mathematical modelling tool, with applications ranging from finance to computational chemistry. However, standard numer... |
Title: Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations Abstract: Despite the plethora of post hoc model explanation methods, the basic properties and behavior of these methods and the conditions under which each one is effective are not well understood. In... |
Title: Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning Abstract: We propose a unifying view to analyze the representation quality of self-supervised learning (SSL) models without access to supervised labels, while being agnostic to the architecture, learning algorithm or data ma... |
Title: Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions Abstract: Generalization is one of the most important problems in deep learning (DL). In the overparameterized regime in neural networks, there exist many low-loss solutions that fit the training data e... |
Title: Equivariant Reinforcement Learning for Quadrotor UAV Abstract: This paper presents an equivariant reinforcement learning framework for quadrotor unmanned aerial vehicles. Successful training of reinforcement learning often requires numerous interactions with the environments, which hinders its applicability espe... |
Title: Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features Abstract: Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks. However, existing metrics for quantifying th... |
Title: Hard Negative Sampling Strategies for Contrastive Representation Learning Abstract: One of the challenges in contrastive learning is the selection of appropriate \textit{hard negative} examples, in the absence of label information. Random sampling or importance sampling methods based on feature similarity often ... |
Title: Uniqueness and Complexity of Inverse MDP Models Abstract: What is the action sequence aa'a" that was likely responsible for reaching state s"' (from state s) in 3 steps? Addressing such questions is important in causal reasoning and in reinforcement learning. Inverse "MDP" models p(aa'a"|ss"') can be used to ans... |
Title: Deep Learning on Implicit Neural Datasets Abstract: Implicit neural representations (INRs) have become fast, lightweight tools for storing continuous data, but to date there is no general method for learning directly with INRs as a data representation. We introduce a principled deep learning framework for learni... |
Title: Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation Abstract: In this paper, we study the problem of sparse mixed linear regression on an unlabeled dataset that is generated from linear measurements from two different regression parameter vectors. Since the data is... |
Title: Invertible Neural Networks for Graph Prediction Abstract: In this work, we address conditional generation using deep invertible neural networks. This is a type of problem where one aims to infer the most probable inputs $X$ given outcomes $Y$. We call our method \textit{invertible graph neural network} (iGNN) du... |
Title: Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning Abstract: In this work, we propose a novel ${\bf K}$ernelized ${\bf S}$tein Discrepancy-based Posterior Sampling for ${\bf RL}$ algorithm (named $\texttt{KSRL}$) which extends model-based RL based upon posteri... |
Title: Causal Structure Learning: a Combinatorial Perspective Abstract: In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on approaches for learning directed acyclic graphs (DAGs) and various generalizations which allow for some variable... |
Title: Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation Abstract: General translation models often still struggle to generate accurate translations in specialized domains. To guide machine translation practitioners and characterize the effectiveness of domain adaptation methods ... |
Title: Vygotskian Autotelic Artificial Intelligence: Language and Culture Internalization for Human-Like AI Abstract: Building autonomous artificial agents able to grow open-ended repertoires of skills is one of the fundamental goals of AI. To that end, a promising developmental approach recommends the design of intrin... |
Title: A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning Abstract: In this paper, we study a large-scale multi-agent minimax optimization problem, which models many interesting applications in statistical learning and game theory, including Generative Adversarial Networks (GANs)... |
Title: Predictive Multiplicity in Probabilistic Classification Abstract: For a prediction task, there may exist multiple models that perform almost equally well. This multiplicity complicates how we typically develop and deploy machine learning models. We study how multiplicity affects predictions -- i.e., predictive m... |
Title: Super-resolving 2D stress tensor field conserving equilibrium constraints using physics informed U-Net Abstract: In a finite element analysis, using a large number of grids is important to obtain accurate results, but is a resource-consuming task. Aiming to real-time simulation and optimization, it is desired to... |
Title: Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages Abstract: Diabetic retinopathy is an eye-related pathology creating abnormalities and causing visual impairment, proper treatment of which requires identifying irregularities. This research uses a hemorrhage detectio... |
Title: Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Space Abstract: Machine learning classifiers have been demonstrated, both empirically and theoretically, to be robust to label noise under certain conditions -- notably the typical assumption is that label noise is independent of... |
Title: Weakly Supervised Representation Learning with Sparse Perturbations Abstract: The theory of representation learning aims to build methods that provably invert the data generating process with minimal domain knowledge or any source of supervision. Most prior approaches require strong distributional assumptions on... |
Title: Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise Abstract: Stochastic first-order methods such as Stochastic Extragradient (SEG) or Stochastic Gradient Descent-Ascent (SGDA) for solving smooth minimax problems and, more generally, variational inequality problems (VIP) have been gai... |
Title: Combining Machine Learning and Agent-Based Modeling to Study Biomedical Systems Abstract: Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from ... |
Title: Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning Abstract: Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disab... |
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... |
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: 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: 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: 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: 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: Detecting the Severity of Major Depressive Disorder from Speech: A Novel HARD-Training Methodology Abstract: Major Depressive Disorder (MDD) is a common worldwide mental health issue with high associated socioeconomic costs. The prediction and automatic detection of MDD can, therefore, make a huge impact on soci... |
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: 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: 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: 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: 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: Excess risk analysis for epistemic uncertainty with application to variational inference Abstract: We analyze the epistemic uncertainty (EU) of supervised learning in Bayesian inference by focusing on the excess risk. Existing analysis is limited to the Bayesian setting, which assumes a correct model and exact B... |
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