Scalable inference for Nonparametric Stochastic Approximation in Reproducing Kernel Hilbert Spaces

Meimei Liu, Zuofeng Shang, Yun Yang

Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression

Yufan Li, Pragya Sur

"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts

Varun Babbar*, Zhicheng Guo*, Cynthia Rudin

The performance of machine learning models relies heavily on the quality of input data, yet real-world applications often face significant data-relate...

Nonparametric Estimation of a Covariate-Adjusted Counterfactual Treatment Regimen Response Curve

Ashkan Ertefaie, Luke Duttweiler, Brent A. Johnson et al.

Optimal Eigenvalue Shrinkage in the Semicircle Limit

Michael Jacob Feldman, David Leigh Donoho

Versatile Differentially Private Learning for General Loss Functions

Song X Chen, Qilong Lu, Yumou Qiu

Analysis of Singular Subspaces Under Random Perturbations

Ke Wang

Large-Scale Multiple Testing: Fundamental Limits of False Discovery Rate Control and Compound Oracle

Yutong Nie, Yihong Wu

Estimation of Grouped Time-Varying Network Vector Autoregressive Models

Degui Li, Bin Peng, Songqiao Tang et al.

Trace Test for High-Dimensional Cointegration

Alexei Onatski, Chen Wang

Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation

Peter Bühlmann, Zhenyu Wang, Zijian Guo

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data

Félix Camirand Lemyre, Raymond J. Carroll, Aurore Delaigle

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Jiancong Xiao, Ziniu Li, Xingyu Xie et al.

Transfer learning under large-scale low-rank regression models

Seyoung Park, Eun Ryung Lee, Hongyu Zhao et al.

Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm

Zhanrui Cai, Xintao Xia, Linjun Zhang

Mixture Models: Parametric, Semiparametric, and New Directions

Hien D. Nguyen

Construction of Asymmetric Nested Orthogonal Arrays

Shanqi Pang, Xiao Lin, Mingyao Ai et al.

SMART-MC: Characterizing the Dynamics of Multiple Sclerosis Therapy Transitions Using a Covariate-Based Markov Model

Beomchang Kim, Zongqi Xia, Priyam Das

Dynamic Decision Making With Individualized Variable Selection

Bryan Cai, Ying Cui, Haoda Fu et al.

Developing A Practical Measure: An Asymmetric Mean Squared Prediction Error for Small Area Estimation

Haiqiang Ma, Thuan Nguyen, Jiming Jiang

Policy Learning with Distributional Welfare

Yifan Cui, Sukjin Han

Efficiency of QMLE for dynamic panel data models with interactive effects

Jushan Bai

A New Approach for Homogeneity Pursuit in Short Panel Data Analysis

Yang Han, Wenyang Zhang, Weichi Wu

Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference

Zhiling Gu, Shan Yu, Guannan Wang et al.

Universally Optimal Designs for Symmetric Models in Order-of-Addition Experiments

Ze Liu, Yongdao Zhou, Min-Qian Liu

istributionally Robust Learning for Multi-source Unsupervised Domain Adaptation

Zijian Guo, Peter Bühlmann, Zhenyu Wang

Linear Separation Capacity of Self-Supervised Representation Learning

Shulei Wang

Recent advances in self-supervised learning have highlighted the efficacy of data augmentation in learning data representation from unlabeled data. Tr...

On the Convergence of Projected Policy Gradient for Any Constant Step Sizes

Zhihua Zhang, Jiacai Liu, Wenye Li et al.

Projected policy gradient (PPG) is a basic policy optimization method in reinforcement learning. Given access to exact policy evaluations, previous s...

Learning with Linear Function Approximations in Mean-Field Control

Erhan Bayraktar, Ali Devran Kara

The paper focuses on mean-field type multi-agent control problems with finite state and action spaces where the dynamics and cost structures are symme...

A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization

Junwen Qiu, Xiao Li, Andre Milzarek

Random reshuffling techniques are prevalent in large-scale applications, such as training neural networks. While the convergence and acceleration effe...

Model-free Change-Point Detection Using AUC of a Classifier

Feiyu Jiang, Rohit Kanrar, Zhanrui Cai

In contemporary data analysis, it is increasingly common to work with non-stationary complex data sets. These data sets typically extend beyond the cl...

EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback

Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov et al.

First proposed by Seide (2014) as a heuristic, error feedback (EF) is a very popular mechanism for enforcing convergence of distributed gradient-based...

Multiple Instance Verification

Xin Xu, Eibe Frank, Geoffrey Holmes

We explore multiple instance verification, a problem setting in which a query instance is verified against a bag of target instances with heterogeneou...

Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

Yang Feng, Yuqi Gu, Ye Tian

Representation multi-task learning (MTL) has achieved tremendous success in practice. However, the theoretical understanding of these methods is still...

Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data

Kean Ming Tan, Yanxin Jin, Yang Ning

Graphical models have been used extensively for modeling brain connectivity networks. However, unmeasured confounders and correlations among measureme...

Optimizing Return Distributions with Distributional Dynamic Programming

Bernardo Ávila Pires, Mark Rowland, Diana Borsa et al.

We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforc...

Imprecise Multi-Armed Bandits: Representing Irreducible Uncertainty as a Zero-Sum Game

Vanessa Kosoy

We introduce a novel multi-armed bandit framework, where each arm is associated with a fixed unknown credal set over the space of outcomes (which can ...

Early Alignment in Two-Layer Networks Training is a Two-Edged Sword

Etienne Boursier, Nicolas Flammarion

Training neural networks with first order optimisation methods is at the core of the empirical success of deep learning. The scale of initialisation i...

Hierarchical Decision Making Based on Structural Information Principles

Xianghua Zeng, Hao Peng, Dingli Su et al.

Hierarchical Reinforcement Learning (HRL) is a promising approach for managing task complexity across multiple levels of abstraction and accelerating ...

Generative Adversarial Networks: Dynamics

Matias G. Delgadino, Bruno B. Suassuna, Rene Cabrera

We study quantitatively the overparametrization limit of the original Wasserstein-GAN algorithm. Effectively, we show that the algorithm is a stochast...

“What is Different Between These Datasets?” A Framework for Explaining Data Distribution Shifts

Varun Babbar*, Zhicheng Guo*, Cynthia Rudin

The performance of machine learning models relies heavily on the quality of input data, yet real-world applications often face significant data-relate...

Assumption-lean and data-adaptive post-prediction inference

Jiacheng Miao, Xinran Miao, Yixuan Wu et al.

A primary challenge facing modern scientific research is the limited availability of gold-standard data, which can be costly, labor-intensive, or inva...

Bagged Regularized k-Distances for Anomaly Detection

Yuchao Cai, Hanfang Yang, Yuheng Ma et al.

We consider the paradigm of unsupervised anomaly detection, which involves the identification of anomalies within a dataset in the absence of labeled ...

Four Axiomatic Characterizations of the Integrated Gradients Attribution Method

Daniel Lundstrom, Meisam Razaviyayn

Deep neural networks have produced significant progress among machine learning models in terms of accuracy and functionality, but their inner workings...

Fast Algorithm for Constrained Linear Inverse Problems

Mohammed Rayyan Sheriff, Floor Fenne Redel, Peyman Mohajerin Esfahani

We consider the constrained Linear Inverse Problem (LIP), where a certain atomic norm (like the $\ell_1 $ norm) is minimized subject to a quadratic co...

High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces

Shihao Shao, Yikang Li, Zhouchen Lin et al.

Irreducible Cartesian tensors (ICTs) play a crucial role in the design of equivariant graph neural networks, as well as in theoretical chemistry and c...

Best Linear Unbiased Estimate from Privatized Contingency Tables

Jordan Awan, Adam Edwards, Paul Bartholomew et al.

In differential privacy (DP) mechanisms, it can be beneficial to release "redundant" outputs, where some quantities can be estimated in multiple ways...

Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data

Thomas Chen, Patrícia Muñoz Ewald

We explicitly construct zero loss neural network classifiers. We write the weight matrices and bias vectors in terms of cumulative parameters, which ...

Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods

Bertille FOLLAIN, Francis BACH

We propose a new method for feature learning and function estimation in supervised learning via regularised empirical risk minimisation. Our approach ...

Data-Driven Performance Guarantees for Classical and Learned Optimizers

Rajiv Sambharya, Bartolomeo Stellato

We introduce a data-driven approach to analyze the performance of continuous optimization algorithms using generalization guarantees from statistical ...

Contextual Bandits with Stage-wise Constraints

Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett

We study contextual bandits in the presence of a stage-wise constraint when the constraint must be satisfied both with high probability and in expecta...

Boosting Causal Additive Models

Maximilian Kertel, Nadja Klein

We present a boosting-based method to learn additive Structural Equation Models (SEMs) from observational data, with a focus on the theoretical aspect...

Frequentist Guarantees of Distributed (Non)-Bayesian Inference

Bohan Wu, César A. Uribe

We establish frequentist properties, i.e., posterior consistency, asymptotic normality, and posterior contraction rates, for the distributed (non-)Bay...

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection

Donglin Zeng, Yufeng Liu, Daiqi Gao

Dynamic treatment regimes or policies are a sequence of decision functions over multiple stages that are tailored to individual features. One importan...

EMaP: Explainable AI with Manifold-based Perturbations

Minh Nhat Vu, Huy Quang Mai, My T. Thai

In the last few years, many explanation methods based on the perturbations of input data have been introduced to shed light on the predictions generat...

Autoencoders in Function Space

Justin Bunker, Mark Girolami, Hefin Lambley et al.

Autoencoders have found widespread application in both their original deterministic form and in their variational formulation (VAEs). In scientific ap...

Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds

Paul Rosa, Judith Rousseau

We consider the nonparametric regression problem when the covariates are located on an unknown compact submanifold of a Euclidean space. Under definin...

System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning

Matteo Bettini, Ajay Shankar, Amanda Prorok

Evolutionary science provides evidence that diversity confers resilience in natural systems. Yet, traditional multi-agent reinforcement learning techn...

Distribution Estimation under the Infinity Norm

Aryeh Kontorovich, Amichai Painsky

We present novel bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These are nearly optimal in various precise se...

Extending Temperature Scaling with Homogenizing Maps

Christopher Qian, Feng Liang, Jason Adams

As machine learning models continue to grow more complex, poor calibration significantly limits the reliability of their predictions. Temperature scal...

Density Estimation Using the Perceptron

Patrik Róbert Gerber, Yury Polyanskiy, Tianze Jiang et al.

We propose a new density estimation algorithm. Given $n$ i.i.d. observations from a distribution belonging to a class of densities on $\mathbb{R}^d$...

Simplex Constrained Sparse Optimization via Tail Screening

Peng Chen, Jin Zhu, Xueqin Wang et al.

We consider the probabilistic simplex-constrained sparse recovery problem. The commonly used Lasso-type penalty for promoting sparsity is ineffective ...

Score-Based Diffusion Models in Function Space

Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista et al.

Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data wit...

Regularized Rényi Divergence Minimization through Bregman Proximal Gradient Algorithms

Thomas Guilmeau, Emilie Chouzenoux, Víctor Elvira

We study the variational inference problem of minimizing a regularized Rényi divergence over an exponential family. We propose to solve this problem w...

WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings

Pablo Badilla, Felipe Bravo-Marquez, María José Zambrano et al.

Word embeddings, which are a mapping of words into continuous vectors, are widely used in modern Natural Language Processing (NLP) systems. However, t...

Frontiers to the learning of nonparametric hidden Markov models

Kweku Abraham, Zacharie Naulet, Elisabeth Gassiat

Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from unknown populations, allowing nonparametric modelling of the ...

On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes

Zhiheng Chen, Guanhua Fang, Wen Yu

Temporal point process (TPP) is an important tool for modeling and predicting irregularly timed events across various domains. Recently, the recurrent...

Classification in the high dimensional Anisotropic mixture framework: A new take on Robust Interpolation

Stanislav Minsker, Mohamed Ndaoud, Yiqiu Shen

We study the classification problem under the two-component anisotropic sub-Gaussian mixture model in high dimensions and in the non-asymptotic settin...

Universal Online Convex Optimization Meets Second-order Bounds

Yibo Wang, Lijun Zhang, Guanghui Wang et al.

Recently, several universal methods have been proposed for online convex optimization, and attain minimax rates for multiple types of convex function...

Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens

Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard

We provide the first known algorithm that provably achieves $\varepsilon$-optimality within $\widetilde{O}(1/\varepsilon)$ function evaluations for th...

Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests

Brian Liu, Rahul Mazumder

We study the often overlooked phenomenon, first noted in Breiman (2001), that random forests appear to reduce bias compared to bagging. Motivated by a...

skglm: Improving scikit-learn for Regularized Generalized Linear Models

Badr Moufad, Pierre-Antoine Bannier, Quentin Bertrand et al.

We introduce skglm, an open-source Python package for regularized Generalized Linear Models. Thanks to its composable nature, it supports combining da...

Losing Momentum in Continuous-time Stochastic Optimisation

Kexin Jin, Jonas Latz, Chenguang Liu et al.

The training of modern machine learning models often consists in solving high-dimensional non-convex optimisation problems that are subject to large-s...

Latent Process Models for Functional Network Data

Peter W. MacDonald, Ji Zhu, Elizaveta Levina

Network data are often sampled with auxiliary information or collected through the observation of a complex system over time, leading to multiple netw...

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

Sudipto Banerjee, Xiang Chen, Ian Frankenburg et al.

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the me...

On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory

Andrea Perin, Stephane Deny

Symmetries (transformations by group actions) are present in many datasets, and leveraging them holds considerable promise for improving predictions i...

Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems

Michal Dereziński, Daniel LeJeune, Deanna Needell et al.

Despite being a key bottleneck in many machine learning tasks, the cost of solving large linear systems has proven challenging to quantify due to prob...

Deep Generative Models: Complexity, Dimensionality, and Approximation

Kevin Wang, Hongqian Niu, Didong Li et al.

Generative networks have shown remarkable success in learning complex data distributions, particularly in generating high-dimensional data from lower-...

ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation

Sungduk Yu, Zeyuan Hu, Akshay Subramaniam et al.

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing cri...

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching

Jannis Chemseddine, Paul Hagemann, Gabriele Steidl et al.

In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its l...

Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses

Eslam Abdelaleem, Ilya Nemenman, K. Michael Martini

Variational dimensionality reduction methods are widely used for their accuracy, generative capabilities, and robustness. We introduce a unifying fram...

Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space

Romain Verdière, Clémentine Prieur, Olivier Zahm

We propose a gradient-enhanced algorithm for high-dimensional function approximation. The algorithm proceeds in two steps: firstly, we reduce the inp...

Finite Expression Method for Solving High-Dimensional Partial Differential Equations

Senwei Liang, Haizhao Yang

Designing efficient and accurate numerical solvers for high-dimensional partial differential equations (PDEs) remains a challenging and important topi...

Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees

Defeng Sun, Yancheng Yuan, Ziwen Wang et al.

In this paper, we propose a randomly projected convex clustering model for clustering a collection of $n$ high dimensional data points in $\mathbb{R}^...

Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary

Tianyang Hu, Ruiqi Liu, Zuofeng Shang et al.

Deep learning has gained huge empirical successes in large-scale classification problems. In contrast, there is a lack of statistical understanding ab...

Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

Lijun Zhang, Yuanyu Wan, Tong Wei et al.

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss f...

Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning

Dennis Chemnitz, Maximilian Engel

For overparameterized optimization tasks, such as those found in modern machine learning, global minima are generally not unique. In order to understa...

PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks

Xiyue Zhang, Benjie Wang, Marta Kwiatkowska et al.

Most methods for neural network verification focus on bounding the image, i.e., set of outputs for a given input set. This can be used to, for example...

Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability

Céline Comte, Matthieu Jonckheere, Jaron Sanders et al.

In this paper, we introduce a policy-gradient method for model-based reinforcement learning (RL) that exploits a type of stationary distributions comm...

On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm

Huan Li, Zhouchen Lin, Yiming Dong

Although adaptive gradient methods have been extensively used in deep learning, their convergence rates proved in the literature are all slower than t...

Categorical Semantics of Compositional Reinforcement Learning

Georgios Bakirtzis, Michail Savvas, Ufuk Topcu

Compositional knowledge representations in reinforcement learning (RL) facilitate modular, interpretable, and safe task specifications. However, gener...

Transformers from Diffusion: A Unified Framework for Neural Message Passing

Qitian Wu, David Wipf, Junchi Yan

Learning representations for structured data with certain geometries (e.g., observed or unobserved) is a fundamental challenge, wherein message passin...

Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning

Yong Lin, Chen Liu, Chenlu Ye et al.

Modern deep learning heavily relies on large labeled datasets, which often comse with high costs in terms of both manual labeling and computational re...

Actor-Critic learning for mean-field control in continuous time

Noufel FRIKHA, Maximilien GERMAIN, Mathieu LAURIERE et al.

We study policy gradient for mean-field control in continuous time in a reinforcement learning setting. By considering randomised policies with entro...

Modelling Populations of Interaction Networks via Distance Metrics

George Bolt, Simón Lunagómez, Christopher Nemeth

Network data arises through the observation of relational information between a collection of entities, for example, friendships (relations) amongst a...

BitNet: 1-bit Pre-training for Large Language Models

Lei Wang, Yi Wu, Hongyu Wang et al.

The increasing size of large language models (LLMs) has posed challenges for deployment and raised concerns about environmental impact due to high ene...

Physics-informed Kernel Learning

Nathan Doumèche, Gérard Biau, Francis Bach et al.

Physics-informed machine learning typically integrates physical priors into the learning process by minimizing a loss function that includes both a da...

Last-iterate Convergence of Shuffling Momentum Gradient Method under the Kurdyka-Lojasiewicz Inequality

Yuqing Liang, Dongpo Xu

Shuffling gradient algorithms are extensively used to solve finite-sum optimization problems in machine learning. However, their theoretical propertie...

Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights

Paul Egels, Ismaël Castillo

We consider deep neural networks in a Bayesian framework with a prior distribution sampling the network weights at random. Following a recent idea of...

Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL

Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi

This paper explores the use of Maximum Causal Entropy Inverse Reinforcement Learning (IRL) within the context of discrete-time stationary Mean-Field G...

Degree of Interference: A General Framework For Causal Inference Under Interference

Yuki Ohnishi, Bikram Karmakar, Arman Sabbaghi

One core assumption typically adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experi...

Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo

Max Hird, Samuel Livingstone

We study linear preconditioning in Markov chain Monte Carlo. We consider the class of well-conditioned distributions, for which several mixing time bo...

Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions

Penghe Zhang, Naihua Xiu, Hou-Duo Qi

The hard-margin loss function has been at the core of the support vector machine research from the very beginning due to its generalization capability...

On Model Identification and Out-of-Sample Prediction of PCR with Applications to Synthetic Controls

Devavrat Shah, Anish Agarwal, Dennis Shen

We analyze principal component regression (PCR) in a high-dimensional error-in-variables setting with fixed design. Under suitable conditions, we show...

Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior

Keru Wu, Ben Wu, Jian Kang

Deep neural networks (DNN) have been widely used in scalar-on-image regression to predict an outcome variable from imaging predictors. However, train...

Confounder Selection via Iterative Graph Expansion

F. Richard Guo, Qingyuan Zhao

Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine*

Canyi Chen, Nan Qiao, Liping Zhu

Simultaneous inference for monotone and smoothly time-varying functions under complex temporal dynamics

Tianpai Luo, Weichi Wu

Chain-linked Multiple Matrix Integration via Embedding Alignment

Runbing Zheng, Minh Tang

Balanced Sampling With Inequalities: Application to Category Bounding, Matrix Rounding, and Spread Sampling

Yves Tillé, Arnaud Tripet

Exponential Families in Theory and Practice

Jessica Gronsbell

Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning

Piyali Basak, Camille Maringe, F. Javier Rubio et al.

Word-Level Maximum Mean Discrepancy Regularization for Word Embedding

Youqian Gao, Ben Dai

Data thinning for Poisson factor models and its applications

Zhijing Wang, Peirong Xu, Hongyu Zhao et al.

Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities

Jian Cao, Matthias Katzfuss

Causality-oriented robustness: exploiting general noise interventions in linear structural causal models

Peter Bühlmann, Xinwei Shen, Armeen Taeb

Confidence Sets for Causal Orderings

Y. Samuel Wang, Mladen Kolar, Mathias Drton

A Bayesian nonparametric approach to mediation and spillover effects with multiple mediators in cluster-randomized trials

Yuki Ohnishi, Fan Li

On the poor statistical properties of theP-curve meta-analytic procedure

Richard D. Morey, Clintin P. Davis-Stober

Inference for Dispersion and Curvature of Random Objects

Hans-Georg Müller, Wookyeong Song

Integrative Analysis of Microbial 16S Gene and Shotgun Metagenomic Sequencing Data Improves Statistical Efficiency in Testing Differential Abundance

Ye Yue, Yicong Mao, Timothy D. Read et al.

The Effect of Alcohol intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data

Shuo Chen, Chixiang Chen, Zhenyao Ye et al.

Optimal Transport based Cross-Domain Integration for Heterogeneous Data

Annie Qu, Babak Shahbaba, Yubai Yuan et al.

Correction

Using Total Margin of Error to Account for Non-Sampling Error in Election Polls

Jeff Dominitz, Charles F. Manski

Inference on the proportion of variance explained in principal component analysis

Ronan Perry, Snigdha Panigrahi, Jacob Bien et al.

A Minimax Two-Sample Test for Functional Data via Grothendieck’s Divergence

Yan Chen, Hongmei Lin, Xueqin Wang et al.

Information Theoretic Limits of Robust Sub-Gaussian Mean Estimation Under Star-Shaped Constraints

Matey Neykov, Akshay Prasadan

Optimality of Approximate Message Passing for Spiked Matrix Models with Rotationally Invariant Noise

Rishabh Dudeja, Songbin Liu, Junjie Ma

Adaptive Robust Confidence Intervals

Yuetian Luo, Chao Gao

Communication-Efficient and Distributed-Oracle Estimation for High-Dimensional Quantile Regression

Songshan Yang, Yifan Gu, Xuming He et al.

Optimal Convex $M$-Estimation via Score Matching

Oliver Y. Feng, Yu-Chun Kao, Min Xu et al.

Semiparametric Bernstein-Von Mises Phenomenon via Isotonized Posterior in Wicksell’s Problem

Francesco Gili, Aad van der Vaart, Geurt Jongbloed

Huber means on Riemannian manifolds

Jongmin LeeandSungkyu Jung

Wasserstein generative regression

Shanshan Songand others

Multilayer random dot product graphs: estimation and online change point detection

Fan Wangand others

Statistical ranking with dynamic covariatesGet access

Pinjun Dongand others

Neural Networks Generalize on Low Complexity Data

Sourav Chatterjee, Timothy Sudijono

Pretraining and the lassoGet access

Erin Craigand others

Censored quantile regression with time-dependent covariates

Chi Wing Chuand others

Online multivariate changepoint detection: leveraging links with computational geometryGet access

Liudmila Pishchaginaand others

Berry-Esseen Bounds for Design-Based Causal Inference With Possibly Diverging Treatment Levels and Varying Group Sizes

Peng Ding, Lei Shi

Multivariate Root-N-Consistent Smoothing Parameter Free Matching Estimators and Estimators of Inverse Density Weighted Expectations

Hajo Holzmann, Alexander Meister

Change Point Estimation for a Stochastic Heat Equation

Markus Reiß, Claudia Strauch, Lukas Trottner

Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift

Kaizheng Wang

A Computational Transition for Detecting Correlated Stochastic Block Models by Low-Degree Polynomials

Jian Ding, Zhangsong Li, Guanyi Chen et al.

Debiased calibration estimation using generalized entropy in survey sampling

Yonghyun Kwon, Jae Kwang Kim, Yumou Qiu

Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition

Andrea Montanari, Yuchen Wu

Effect Aliasing in Observational Studies

Paul R. Rosenbaum, José R. Zubizarreta

Online Policy Learning and Inference by Matrix Completion

Dong Xia, Jingyang Li, Congyuan Duan

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference

Shuting Shen, Junwei Lu, Xihong Lin

Inference for Low-rank Models without Estimating the Rank

Jungjun Choi, Hyukjun Kwon, Yuan Liao

A powerful transformation of quantitative responses for biobank-scale association studies

Yaowu Liu, Tianying Wang

Kernel Spectral Joint Embeddings for High-Dimensional Noisy Datasets using Duo-Landmark Integral Operators

Xiucai Ding, Rong Ma

Principal stratification with continuous post-treatment variables: nonparametric identification and semiparametric estimation

Sizhu Lu, others

Solving the Poisson Equation Using Coupled Markov Chains

Pierre Etienne Jacob, Randal Douc, Anthony Lee et al.

DRM Revisited: A Complete Error Analysis

Yuling Jiao, Ruoxuan Li, Peiying Wu et al.

It is widely known that the error analysis for deep learning involves approximation, statistical, and optimization errors. However, it is challenging ...

Average Partial Effect Estimation Using Double Machine Learning

Harvey Klyne, Rajen Shah

Fundamental Limits of Community Detection From Multi-View Data: Multi-Layer, Dynamic and Partially Labeled Block Models

Subhabrata Sen, Xiaodong Yang, Buyu Lin

Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data

Tianyu Zhang, Jing Lei

Poisson Empirical Bayes Estimation: When Doesg-Modeling Beatf-Modeling in Theory (And in Practice)?

Yihong Wu, Yandi Shen

High-Dimensional Hilbert-Schmidt Linear Regression with Hilbert Manifold Variables

Changwon Choi, Byeong U. Park

Optimal Sequencing Depth for Single-Cell RNA-Sequencing in Wasserstein Space

Jakwang Kim, Sharvaj Kubal, Geoffrey Schiebinger

A Two-Way Heterogeneity Model for Dynamic Networks

Binyan Jiang, Ting Yan, Qiwei Yao et al.

A Geometrical Analysis of Kernel Ridge Regression and its Applications

Zong Shang, Guillaume Lecué, Georgios Gavrilopoulos

Kurtosis-Based Projection Pursuit for Matrix-Valued Data

Una Radojicic, Klaus Nordhausen, Joni Virta

Clustering by Hill-Climbing: Consistency Results

Ery Arias-Castro, Wanli Qiao

A Flexible Defense Against the Winner’s Curse

William Fithian, Tijana Zrnic

Rank Tests for PCA Under Weak Identifiability

Davy Paindaveine, Laura Peralvo Maroto, Thomas Verdebout

Sparse PCA: A New Scalable Estimator Based on Integer Programming

Kayhan Behdin, Rahul Mazumder

Semi-Supervised U-Statistics

Larry Wasserman, Ilmun Kim, Sivaraman Balakrishnan et al.

Scalable Inference in Functional Linear Regression with Streaming Data

Linglong Kong, Jinhan Xie, Enze Shi et al.

The Empirical Copula Process in High Dimensions: Stute’s Representation and Applications

Axel Bücher, Cambyse Pakzad

Causal Effect Estimation Under Network Interference with Mean-Field Methods

Subhabrata Sen, Sohom Bhattacharya

Clustering risk in Non-parametric Hidden Markov and I.I.D. Models

Elisabeth Gassiat, Ibrahim Kaddouri, Zacharie Naulet

Efficiently Matching Random Inhomogeneous Graphs via Degree Profiles

Jian Ding, Yumou Fei, Yuanzheng Wang

Entrywise Dynamics and Universality of General First Order Methods

Qiyang Han

Improving Knockoffs with Conditional Calibration

William Fithian, Yixiang Luo, Lihua Lei

Spectral Density Estimation of Function-Valued Spatial Processes

Rafail Kartsioukas, Stilian Stoev, Tailen Hsing

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

Jianqing Fan, Yihong Gu, Cong Fang et al.

Tests of Missing Completely at Random Based on Sample Covariance Matrices

Alberto Bordino, Thomas Benjamin Berrett

Near Optimal Sample Complexity for Matrix and Tensor Normal Models via Geodesic Convexity

Rafael Mendes de Oliveira, William Cole Franks, Akshay Ramachandran et al.

Yurinskii’s Coupling for Martingales

Matias Damian Cattaneo, Ricardo Pereira Masini, William George Underwood

Improved Learning Theory for Kernel Distribution Regression with Two-Stage Sampling

François Bachoc, Louis Béthune, Alberto González-Sanz et al.

Trimmed Sample Means for Robust Uniform Mean Estimation and Regression

Roberto Imbuzeiro Moraes Felinto de Oliveira, Lucas Resende

Pseudo-Likelihood-Based M-Estimation of Random Graphs with Dependent Edges and Parameter Vectors of Increasing Dimension

Jonathan Roy Stewart, Michael Schweinberger

Robust Transfer Learning with Unreliable Source Data

Jianqing Fan, Cheng Gao, Jason Matthew Klusowski

Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning

Xi Chen, Yichen Zhang, Weidong Liu et al.

The High-Dimensional Asymptotics of Principal Component Regression

Alden Green, Elad Romanov

Theory of Functional Principal Component Analysis for Discretely Observed Data

Fang Yao, Hang Zhou, Dongyi Wei

A Unified Analysis of Likelihood-based Estimators in the Plackett–Luce Model

Ruijian Han, Yiming Xu

Symmetry: A General Structure in Nonparametric Regression

Louis Goldwater Christie, John A. D. Aston

Advances in Bayesian Model Selection Consistency for High-Dimensional Generalized Linear Models

Jeyong Lee, Minwoo Chae, Ryan Martin

Estimation and Inference in Distributional Reinforcement Learning

Liangyu Zhang, Yang Peng, Jiadong Liang et al.

Online Statistical Inference in Decision Making with Matrix Context

Yichen Zhang, Qiyu Han, Will Wei Sun

Structured Matrix Learning under Arbitrary Entrywise Dependence and Estimation of Markov Transition Kernel

Jianqing Fan, Jinhang Chai

Optimal and Exact Recovery on the General Non-Uniform Hypergraph Stochastic Block Model

Ioana Dumitriu, Hai-Xiao Wang

High-Dimensional Statistical Inference for Linkage Disequilibrium Score Regression and Its Cross-Ancestry Extensions

Fei Xue, Bingxin Zhao

Deep Horseshoe Gaussian Processes

Ismaël Castillo, Thibault Christophe Randrianarisoa

The Functional Graphical Lasso

Kartik Govind Waghmare, Tomas Masak, Victor Michael Panaretos

Higher-Order Entrywise Eigenvectors Analysis of Low-Rank Random Matrices: Bias Correction, Edgeworth Expansion, and Bootstrap

Yichi Zhang, Fangzheng Xie

Counterfactual Inference in Sequential Experiments

Raaz Dwivedi, Katherine Tian, Sabina Tomkins et al.

Optimal Vintage Factor Analysis with Deflation Varimax

Xin Bing, Xin He, Dian Jin et al.

Low-Degree Hardness of Detection for Correlated Erdős-Rényi Graphs

Jian Ding, Zhangsong Li, Hang Du

Spectral Gap Bounds for Reversible Hybrid Gibbs Chains

Qian Qin, Nianqiao Ju, Guanyang Wang

Fixed and Random Covariance Regression Analyses

Wei Lan, Chih-Ling Tsai, Runze Li et al.

Asymptotic Theory of Geometric and Adaptive $k$-Means Clustering

Adam Quinn Jaffe

Debiased Regression Adjustment in Completely Randomized Experiments with Moderately High-Dimensional Covariates

Xin Lu, Fan Yang, Yuhao Wang

Reinforcement Learning for Individual Optimal Policy From Heterogeneous Data

Annie Qu, Rui Miao, Babak Shahbaba

Policy Learning “Without” Overlap: Pessimism and Generalized Empirical Bernstein’s Inequality

Zhaoran Wang, Ying Jin, Zhimei Ren et al.

Algorithmic Stability Implies Training-Conditional Coverage for Distribution-Free Prediction Methods

Ruiting Liang, Rina Foygel Barber

On the Multiway Principal Component Analysis

Ming Yuan, Jialin Ouyang

Semiparametric Modeling and Analysis for Longitudinal Network Data

Yang Feng, Yinqiu He, Jiajin Sun et al.

On the Structural Dimension of Sliced Inverse Regression

Dongming Huang, Songtao Tian, Qian Lin

Erratum: Quantile Processes and Their Applications in Finite Populations

Anurag Dey, Probal Chaudhuri

Dualizing Le Cam’s Method for Functional Estimation, with Applications to Estimating the Unseens

Yihong Wu, Yury Polyanskiy

Asymptotically-Exact Selective Inference for Quantile Regression

Xuming He, Yumeng Wang, Snigdha Panigrahi

Entropic Covariance Models

Piotr Zwiernik

Near-Optimal Inference in Adaptive Linear Regression

Koulik Khamaru, Yash Deshpande, Tor Lattimore et al.

Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF

Zhuoran Yang, Han Shen, Tianyi Chen

Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised lear...

A Common-Cause Principle for Eliminating Selection Bias in Causal Estimands Through Covariate Adjustment

Ilya Shpitser, Maya Mathur, Tyler VanderWeele

Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

Fan Yang, Hongyang R. Zhang, Sen Wu et al.

The problem of learning one task using samples from another task is central to transfer learning. In this paper, we focus on answering the following q...

Score-based Causal Representation Learning: Linear and General Transformations

Burak Var{{\i}}c{{\i}}, Emre Acartürk, Karthikeyan Shanmugam et al.

This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transfor...

On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension

Saptarshi Chakraborty, Peter L. Bartlett

Despite the remarkable empirical successes of Generative Adversarial Networks (GANs), the theoretical guarantees for their statistical accuracy remain...

Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms

Keru Wu, Yuansi Chen, Wooseok Ha et al.

Domain adaptation (DA) is a statistical learning problem that arises when the distribution of the source data used to train a model differs from that ...

Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles

Lesi Chen, Yaohua Ma, Jingzhao Zhang

In this work, we consider bilevel optimization when the lower-level problem is strongly convex. Recent works show that with a Hessian-vector product (...

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

Shao-Bo Lin, Xiaotong Liu, Di Wang et al.

Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for ...

On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control

Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel et al.

A classical approach for solving discrete time nonlinear control on a finite horizon consists in repeatedly minimizing linear quadratic approximations...

A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds

Lei Wang, Le Bao, Xin Liu

This paper focuses on minimizing a smooth function combined with a nonsmooth regularization term on a compact Riemannian submanifold embedded in the E...

Learning conditional distributions on continuous spaces

Cyril Benezet, Ziteng Cheng, Sebastian Jaimungal

We investigate sample-based learning of conditional distributions on multi-dimensional unit boxes, allowing for different dimensions of the feature an...

A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs

Lukas Zierahn, Dirk van der Hoeven, Tal Lancewicki et al.

We derive a new analysis of Follow The Regularized Leader (FTRL) for online learning with delayed bandit feedback. By separating the cost of delayed f...

Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities

Rocco Caprio, Juan Kuntz, Samuel Power et al.

We derive non-asymptotic error bounds for particle gradient descent (PGD, Kuntz et al. (2023)), a recently introduced algorithm for maximum likelihoo...

Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors

Kean Ming Tan, Wen-Xin Zhou, Gaoyu Wu et al.

Expected shortfall (ES) is widely used for characterizing the tail of a distribution across various fields, particularly in financial risk management....

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito et al.

In this work we consider the problem of numerical integration, i.e., approximating integrals with respect to a target probability measure using only p...

Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters

Florian Brück, Jean-David Fermanian, Aleksey Min

There exist several testing procedures based on the maximum mean discrepancy (MMD) to address the challenge of model specification. However, these tes...

Statistical field theory for Markov decision processes under uncertainty

George Stamatescu

A statistical field theory is introduced for finite state and action Markov decision processes with unknown parameters, in a Bayesian setting. The Bel...

Bayesian Data Sketching for Varying Coefficient Regression Models

Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee

Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received ...

Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets

Hanyuan Hang

In this paper, we propose an ensemble learning algorithm named bagged $k$-distance for mode-based clustering (BDMBC) by putting forward a new measure ...

Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals

David Bolin, Vaibhav Mehandiratta, Alexandre B. Simas

The computational cost for inference and prediction of statistical models based on Gaussian processes with Matérn covariance functions scales cubicall...

Invariant Subspace Decomposition

Margherita Lazzaretto, Jonas Peters, Niklas Pfister

We consider the task of predicting a response $Y$ from a set of covariates $X$ in settings where the conditional distribution of $Y$ given $X$ changes...

Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights

Insung Kong, Yongdai Kim

Bayesian approaches for training deep neural networks (BNNs) have received significant interest and have been effectively utilized in a wide range of ...

Outlier Robust and Sparse Estimation of Linear Regression Coefficients

Takeyuki Sasai, Hironori Fujisawa

We consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial...

Affine Rank Minimization via Asymptotic Log-Det Iteratively Reweighted Least Squares

Sebastian Krämer

The affine rank minimization problem is a well-known approach to matrix recovery. While there are various surrogates to this NP-hard problem, we prove...

Causal Effect of Functional Treatment

Ruoxu Tan, Wei Huang, Zheng Zhang et al.

We study the causal effect with a functional treatment variable, where practical applications often arise in neuroscience, biomedical sciences, etc. P...

Uplift Model Evaluation with Ordinal Dominance Graphs

Brecht Verbeken, Marie-Anne Guerry, Wouter Verbeke et al.

Uplift modelling is a subfield of causal learning that focuses on ranking entities by individual treatment effects. Uplift models are typically evalua...

High-Dimensional L2-Boosting: Rate of Convergence

Ye Luo, Martin Spindler, Jannis Kueck

Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of L2-Boosting in a high-dimensio...

Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers

Federico Bassetti, Marco Gherardi, Alessandro Ingrosso et al.

Deep linear networks have been extensively studied, as they provide simplified models of deep learning. However, little is known in the case of finite...

How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences

Miko{\l}aj J. Kasprzak, Ryan Giordano, Tamara Broderick

The Laplace approximation is a popular method for constructing a Gaussian approximation to the Bayesian posterior and thereby approximating the poster...

Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test

Alden Green, Seunghoon Paik, Michael Celentano et al.

Integral probability metrics (IPMs) constitute a general class of nonparametric two-sample tests that are based on maximizing the mean difference betw...

On Inference for the Support Vector Machine

Wen-Xin Zhou, Jakub Rybak, Heather Battey

The linear support vector machine has a parametrised decision boundary. The paper considers inference for the corresponding parameters, which indicate...

Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis

Hongru Yang, Yingbin Liang, Xiaojie Guo et al.

It has been observed that applying pruning-at-initialization methods and training the sparse networks can sometimes yield slightly better test perform...

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

Atticus Geiger, Duligur Ibeling, Amir Zur et al.

Causal abstraction provides a theoretical foundation for mechanistic interpretability, the field concerned with providing intelligible algorithms that...

Implicit vs Unfolded Graph Neural Networks

Yongyi Yang, Tang Liu, Yangkun Wang et al.

It has been observed that message-passing graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient / scalabl...

Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification

Brendon G. Anderson, Ziye Ma, Jingqi Li et al.

In this paper, we study certifying the robustness of ReLU neural networks against adversarial input perturbations. To diminish the relaxation error su...

GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia

Carlo Lucibello, Aurora Rossi

GraphNeuralNetworks.jl is an open-source framework for deep learning on graphs, written in the Julia programming language. It supports multiple GPU ba...

Dynamic angular synchronization under smoothness constraints

Ernesto Araya, Mihai Cucuringu, Hemant Tyagi

Given an undirected measurement graph $\mathcal{H} = ([n], \mathcal{E})$, the classical angular synchronization problem consists of recovering unkno...

Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems

Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas

We propose an operator learning approach to accelerate geometric Markov chain Monte Carlo (MCMC) for solving infinite-dimensional Bayesian inverse pro...

Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds

Hongzhe Li, Haoshu Xu

This paper addresses regression analysis for covariance matrix-valued outcomes with Euclidean covariates, motivated by applications in single-cell gen...

Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis

Youcheng Niu, Jinming Xu, Ying Sun et al.

This paper considers solving a class of nonconvex-strongly-convex distributed stochastic bilevel optimization (DSBO) problems with personalized inner-...

Learning causal graphs via nonlinear sufficient dimension reduction

Eftychia Solea, Bing Li, Kyongwon Kim

We introduce a new nonparametric methodology for estimating a directed acyclic graph (DAG) from observational data. Our method is nonparametric in nat...

On Consistent Bayesian Inference from Synthetic Data

Ossi Räisä, Joonas Jälkö, Antti Honkela

Generating synthetic data, with or without differential privacy, has attracted significant attention as a potential solution to the dilemma between ma...

Optimization Over a Probability Simplex

James Chok, Geoffrey M. Vasil

We propose a new iteration scheme, the Cauchy-Simplex, to optimize convex problems over the probability simplex $\{w\in\mathbb{R}^n\ |\ \sum_i w_i=1\ ...

Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method

Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton et al.

We study approximations to the Moreau envelope---and infimal convolutions more broadly---based on Laplace's method, a classical tool in analysis which...

Sampling and Estimation on Manifolds using the Langevin Diffusion

Karthik Bharath, Alexander Lewis, Akash Sharma et al.

Error bounds are derived for sampling and estimation using a discretization of an intrinsically defined Langevin diffusion with invariant measure $\te...

Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

Yipeng Li, Xinchen Lyu

There are two paradigms in Federated Learning (FL): parallel FL (PFL), where models are trained in a parallel manner across clients, and sequential FL...

Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization

Yaoyu Zhang, Leyang Zhang, Zhongwang Zhang et al.

Determining whether deep neural network (DNN) models can reliably recover target functions at overparameterization is a critical yet complex issue in ...

Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy

Chengli Tan, Jiangshe Zhang, Junmin Liu et al.

Recently, sharpness-aware minimization (SAM) has attracted much attention because of its surprising effectiveness in improving generalization performa...

Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process

Feifei Wang, Zimeng Zhao, Ruimin Ye et al.

Identifying change points in dynamic text data is crucial for understanding the evolving nature of topics across various sources, such as news article...

Deletion Robust Non-Monotone Submodular Maximization over Matroids

Paul Dütting, Federico Fusco, Silvio Lattanzi et al.

We study the deletion robust version of submodular maximization under matroid constraints. The goal is to extract a small-size summary of the data set...

Instability, Computational Efficiency and Statistical Accuracy

Raaz Dwivedi, Koulik Khamaru, Martin J. Wainwright et al.

Many statistical estimators are defined as the fixed point of a data-dependent operator, with estimators based on minimizing a cost function being an ...

Estimation of Local Geometric Structure on Manifolds from Noisy Data

Yariv Aizenbud, Barak Sober

A common observation in data-driven applications is that high-dimensional data have a low intrinsic dimension, at least locally. In this work, we cons...

Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python

Caglar Demir, Alkid Baci, N'Dah Jean Kouagou et al.

In this paper, we present Ontolearn---a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implem...

Continuously evolving rewards in an open-ended environment

Richard M. Bailey

Unambiguous identification of the rewards driving behaviours of entities operating in complex open-ended real-world environments is difficult, in part...

Recursive Causal Discovery

Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari et al.

Causal discovery from observational data, i.e., learning the causal graph from a finite set of samples from the joint distribution of the variables, i...

Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings

Henrik von Kleist, Alireza Zamanian, Ilya Shpitser et al.

Machine learning methods often assume that input features are available at no cost. However, in domains like healthcare, where acquiring features coul...

On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations

Antoine Godichon-Baggioni, Nicklas Werge

Stochastic optimization methods face new challenges in the realm of streaming data, characterized by a continuous flow of large, high-dimensional data...

Determine the Number of States in Hidden Markov Models via Marginal Likelihood

Yang Chen, Cheng-Der Fuh, Chu-Lan Michael Kao

Hidden Markov models (HMM) have been widely used by scientists to model stochastic systems: the underlying process is a discrete Markov chain, and the...

Variance-Aware Estimation of Kernel Mean Embedding

Geoffrey Wolfer, Pierre Alquier

An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded ...

Scaling ResNets in the Large-depth Regime

Pierre Marion, Adeline Fermanian, Gérard Biau et al.

Deep ResNets are recognized for achieving state-of-the-art results in complex machine learning tasks. However, the remarkable performance of these arc...

A Comparative Evaluation of Quantification Methods

Tobias Schumacher, Markus Strohmaier, Florian Lemmerich

Quantification represents the problem of estimating the distribution of class labels on unseen data. It also represents a growing research field in su...

Lightning UQ Box: Uncertainty Quantification for Neural Networks

Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski et al.

Although neural networks have shown impressive results in a multitude of application domains, the "black box" nature of deep learning and lack of conf...

Scaling Data-Constrained Language Models

Niklas Muennighoff, Alexander M. Rush, Boaz Barak et al.

The current trend of scaling language models involves increasing both parameter count and training data set size. Extrapolating this trend suggests th...

Curvature-based Clustering on Graphs

Zachary Lubberts, Yu Tian, Melanie Weber

Unsupervised node clustering (or community detection) is a classical graph learning task. In this paper, we study algorithms that exploit the geometry...

Composite Goodness-of-fit Tests with Kernels

Oscar Key, Arthur Gretton, François-Xavier Briol et al.

We propose kernel-based hypothesis tests for the challenging composite testing problem, where we are interested in whether the data comes from any dis...

PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark

Yang Liu, Jianqing Zhang, Yang Hua et al.

Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection,...

The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning

Wooseok Ha, Bin Yu, Nikhil Ghosh et al.

In this work, we investigate the dynamics of stochastic gradient descent (SGD) when training a single-neuron autoencoder with linear or ReLU activatio...

Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data

Pan Zhao, Shu Yang, Julie Josse

An individualized treatment regime (ITR) is a decision rule that assigns treatments based on patients' characteristics. The value function of an ITR i...

DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

Xiangdong Xie, Jiahua Guo, Yi Sun

Bayesian networks (BNs) are a powerful tool for knowledge representation and reasoning, especially for complex systems. A critical task in the applic...

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

Shouri Hu, Haowei Wang, Zhongxiang Dai et al.

The expected improvement (EI) is one of the most popular acquisition functions for Bayesian optimization (BO) and has demonstrated good empirical perf...

Manifold Fitting under Unbounded Noise

Zhigang Yao, Yuqing Xia

In the field of non-Euclidean statistical analysis, a trend has emerged in recent times, of attempts to recover a low dimensional structure, namely a ...

Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play

Zelai Xu, Chao Yu, Yancheng Liang et al.

Self-play (SP) is a popular multi-agent reinforcement learning framework for competitive games. Despite the empirical success, the theoretical propert...

Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models

Xuefeng Gao, Hoang M. Nguyen, Lingjiong Zhu

Score-based generative models are a recent class of deep generative models with state-of-the-art performance in many applications. In this paper, we e...

Extremal graphical modeling with latent variables via convex optimization

Sebastian Engelke, Armeen Taeb

Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk ...

On the Approximation of Kernel functions

Paul Dommel, Alois Pichler

Various methods in statistical learning build on kernels considered in reproducing kernel Hilbert spaces. In applications, the kernel is often selecte...

Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response

Jue Hou, Tianxi Cai, Rajarshi Mukherjee

A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect assessment is the lack of precise information on important clin...

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning

Kuangyu Ding, Jingyang Li, Kim-Chuan Toh

Stochastic gradient methods for minimizing nonconvex composite objective functions typically rely on the Lipschitz smoothness of the differentiable pa...

Optimizing Data Collection for Machine Learning

Rafid Mahmood, James Lucas, Jose M. Alvarez et al.

Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data t...

Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective

Shayan Hundrieser, Florian Heinemann, Marcel Klatt et al.

We analyze statistical properties of plug-in estimators for unbalanced optimal transport quantities between finitely supported measures in different p...

Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

Jiajing Zheng, Alexander D'Amour, Alexander Franks

Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Buildin...

Rank-one Convexification for Sparse Regression

Alper Atamturk, Andres Gomez

Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact m...

gsplat: An Open-Source Library for Gaussian Splatting

Vickie Ye, Ruilong Li, Justin Kerr et al.

gsplat is an open-source library designed for training and developing Gaussian Splatting methods. It features a front-end with Python bindings compati...

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming

Sen Na, Michael Mahoney

We consider online statistical inference of constrained stochastic nonlinear optimization problems. We apply the Stochastic Sequential Quadratic Progr...

Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds

Clément Bonet, Lucas Drumetz, Nicolas Courty

While many Machine Learning methods have been developed or transposed on Riemannian manifolds to tackle data with known non-Euclidean geometry, Optima...

Accelerating optimization over the space of probability measures

Shi Chen, Qin Li, Oliver Tse et al.

The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine ...

Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data

Sudipto Banerjee, Didong Li, Andrew Jones et al.

Gaussian processes are pervasive in functional data analysis, machine learning, and spatial statistics for modeling complex dependencies. Scientific d...

Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power

Jia He, Maggie Cheng

Graph neural network (GNN) models have been widely used for learning graph-structured data. Due to the permutation-invariant requirement of graph lear...

Optimal Experiment Design for Causal Effect Identification

Negar Kiyavash, Sina Akbari, Jalal Etesami

Pearl’s do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not iden...

Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data

Jie Peng, Weiyu Li, Stefan Vlaski et al.

Robustness to malicious attacks is of paramount importance for distributed learning. Existing works usually consider the classical Byzantine attacks m...

The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond

Jiin Woo, Gauri Joshi, Yuejie Chi

In this paper, we consider federated Q-learning, which aims to learn an optimal Q-function by periodically aggregating local Q-estimates trained on lo...

depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers

Kaichao You, Runsheng Bai, Meng Cao et al.

PyTorch 2.x introduces a compiler designed to accelerate deep learning programs. However, for machine learning researchers, fully leveraging the PyTor...

The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise

Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang

Stochastic approximation is a class of algorithms that update a vector iteratively, incrementally, and stochastically, including, e.g., stochastic gra...

Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick

Xiyuan Wang, Pan Li, Muhan Zhang

In this paper, we study using graph neural networks (GNNs) for multi-node representation learning, where a representation for a set of more than one n...

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

Wei Jin, Yang Ni, Amanda B. Spence et al.

We consider the problem of causal discovery from longitudinal observational data. We develop a novel framework that simultaneously discovers the time-...

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

Yanxun Xu, Fangzheng Xie, Dapeng Yao

We study the sparse high-dimensional Gaussian mixture model when the number of clusters is allowed to grow with the sample size. A minimax lower bound...

Regularizing Hard Examples Improves Adversarial Robustness

Hyungyu Lee, Saehyung Lee, Ho Bae et al.

Recent studies have validated that pruning hard-to-learn examples from training improves the generalization performance of neural networks (NNs). In t...

Random ReLU Neural Networks as Non-Gaussian Processes

Rahul Parhi, Pakshal Bohra, Ayoub El Biari et al.

We consider a large class of shallow neural networks with randomly initialized parameters and rectified linear unit activation functions. We prove tha...

Riemannian Bilevel Optimization

Jiaxiang Li, Shiqian Ma

In this work, we consider the bilevel optimization problem on Riemannian manifolds. We inspect the calculation of the hypergradient of such problems o...

Supervised Learning with Evolving Tasks and Performance Guarantees

Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano

Multiple supervised learning scenarios are composed by a sequence of classification tasks. For instance, multi-task learning and continual learning ai...

Error estimation and adaptive tuning for unregularized robust M-estimator

Pierre C. Bellec, Takuya Koriyama

We consider unregularized robust M-estimators for linear models under Gaussian design and heavy-tailed noise, in the proportional asymptotics regime w...

From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

Xinghao Qiao, Dong Li, Shaojun Guo et al.

Nonparametric estimation of the mean and covariance functions is ubiquitous in functional data analysis and local linear smoothing techniques are most...

Locally Private Causal Inference for Randomized Experiments

Jordan Awan, Yuki Ohnishi

Local differential privacy is a differential privacy paradigm in which individuals first apply a privacy mechanism to their data (often by adding nois...

Estimating Network-Mediated Causal Effects via Principal Components Network Regression

Alex Hayes, Mark M. Fredrickson, Keith Levin

We develop a method to decompose causal effects on a social network into an indirect effect mediated by the network, and a direct effect independent o...

Selective Inference with Distributed Data

Snigdha Panigrahi, Sifan Liu

When data are distributed across multiple sites or machines rather than centralized in one location, researchers face the challenge of extracting mean...

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

Michael I. Jordan, Tianyi Lin, Chi Jin

We provide a unified analysis of two-timescale gradient descent ascent (TTGDA) for solving structured nonconvex minimax optimization problems in the f...

An Axiomatic Definition of Hierarchical Clustering

Ery Arias-Castro, Elizabeth Coda

In this paper, we take an axiomatic approach to defining a population hierarchical clustering for piecewise constant densities, and in a similar manne...

Test-Time Training on Video Streams

Renhao Wang, Yu Sun, Arnuv Tandon et al.

Prior work has established Test-Time Training (TTT) as a general framework to further improve a trained model at test time. Before making a prediction...

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

Boxin Zhao, Lingxiao Wang, Ziqi Liu et al.

Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training....

A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation

Hugo Lebeau, Florent Chatelain, Romain Couillet

This work presents a comprehensive understanding of the estimation of a planted low-rank signal from a general spiked tensor model near the computatio...

Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents

Marco Pleines, Matthias Pallasch, Frank Zimmer et al.

Memory Gym presents a suite of 2D partially observable environments, namely Mortar Mayhem, Mystery Path, and Searing Spotlights, designed to benchmark...

Enhancing Graph Representation Learning with Localized Topological Features

Zuoyu Yan, Qi Zhao, Ze Ye et al.

Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for grap...

Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization

Antoine de Mathelin, François Deheeger, Mathilde Mougeot et al.

This paper deals with uncertainty quantification and out-of-distribution detection in deep learning using Bayesian and ensemble methods. It proposes a...

DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

Jiayi Tong, Jie Hu, George Hripcsak et al.

High-dimensional healthcare data, such as electronic health records (EHR) data and claims data, present two primary challenges due to the large number...

Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes

Pierre Alquier, Charles Riou, Badr-Eddine Chérief-Abdellatif

Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, under this condition, the Gibbs posterior with ...

Efficiently Escaping Saddle Points in Bilevel Optimization

Shiqian Ma, Minhui Huang, Xuxing Chen et al.

Bilevel optimization is one of the fundamental problems in machine learning and optimization. Recent theoretical developments in bilevel optimization ...

Improved bounds and inference on optimal regimes

Julien D. Laurendeau, Aaron L. Sarvet, Mats J. Stensrud

Resampling Methods with Multiply Imputed Data

Michael W Robbins, Lane Burgette

Abstract

Doubly robust conditional independence testing with generative neural networks

Yi Zhang, others

Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels

Joshua Agterberg, Zachary Lubberts, Jesús Arroyo

Estimating maximal symmetries of regression functions via subgroup lattices

Louis G Christie, John A D Aston

A Goodness-of-Fit Assessment for General Learning Procedures in High Dimensions*

Chenxuan He, Canyi Chen, Liping Zhu

Estimation of Out-of-Sample Sharpe Ratio for High Dimensional Portfolio Optimization

Xuran Meng, Yuan Cao, Weichen Wang

Fast Approximation of Shapley Values through Fractional Factorial Designs

Zheng Zhou, Robert Mee, Herbert Hamers et al.

Harmonized estimation of subgroup-specific treatment effects in randomized trials: the use of external control data

Daniel Schwartz, others

SOFARI: High-Dimensional Manifold-Based Inference

Zemin Zheng, Xin Zhou, Yingying Fan et al.

Evaluation of binary classifiers for asymptotically dependent and independent extremes

Juliette Legrand, Philippe Naveau, Marco Oesting

Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma

Kevin Li, Simon Mak, J.-F. Paquet et al.

Asymptotic Guarantees for Bayesian Phylogenetic Tree Reconstruction

Alisa Kirichenko, Luke J. Kelly, Jere Koskela

Conformal Prediction for Network-Assisted Regression

Robert Lunde, Elizaveta Levina, Ji Zhu

Identifying the Structure of High-Dimensional Time Series via Eigen-Analysis

Bo Zhang, Jiti Gao, Guangming Pan et al.

Infinite joint species distribution models

F Stolf, D B Dunson

Abstract

Existence and Applications of Finite Population Samples that are Exactly Balanced

Yves Tillé, Louis-Paul Rivest

Abstract

Partially Exchangeable Stochastic Block Models for (Node-Colored) Multilayer Networks

Francesco Gaffi, Daniele Durante, Antonio Lijoi et al.

Decomposing Gaussians with Unknown Covariance

A Dharamshi, others

Abstract

Dimension estimation in a spiked covariance model using high-dimensional data augmentation

U Radojičić, J Virta

Abstract

Factor pre-training in Bayesian multivariate logistic models

D B Dunson, L Mauri

Abstract

The inverse Kalman filter

Xinyi Fang, Mengyang Gu

Abstract

A More Robust Approach to Multivariable Mendelian Randomization

Yinxiang Wu, others

Abstract

Efficient nonparametric estimators of discrimination measures with censored survival data

Marie Skov Breum, Torben Martinussen

Empirical likelihood for manifolds

Daisuke Kurisu, Taisuke Otsu

The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review

Jianqing Fan, Yuling Yan, Buxin Su et al.

Manipulating an Instrumental Variable in an Observational Study of Premature Babies: Design, Bounds, and Inference

Bo Zhang, Zhe Chen, Min Haeng Cho

Mutually Exciting Point Processes with Latency

Yoann Potiron, Vladimir Volkov

A Latent Variable Model for Individual Degree Measures in Respondent-Driven Sampling

Yibo Wang, Sunghee Lee, Michael R. Elliott

Design and analysis of randomized trials to estimate spatio-temporally heterogeneous treatment effects

Samuel I. Watson, Thomas A. Smith

LAMBDA: A Large Model Based Data Agent

Binyan Jiang, Ruijian Han, Sun Maojun et al.

Deep Fréchet Regression

Su I Iao, Yidong Zhou, Hans-Georg Müller

Deep Mutual Density Ratio Estimation with Bregman Divergence and Its Applications

Jian Huang, Dongxiao Han, Siming Zheng et al.

Higher Order Accurate Symmetric Bootstrap Confidence Intervals in High Dimensional Penalized Regression

Debraj Das, Arindam Chatterjee, S. N. Lahiri

Adjacency Matrix Decomposition Clustering for Human Activity Data

Martha Barnard, Yingling Fan, Julian Wolfson

Debiased learning of the causal net benefit with censored event time data

Torben Martinussen, Stijn Vansteelandt

Abstract

Statistical Quantile Learning for Large Additive Latent Variable Models

Julien Bodelet, Guillaume Blanc, Jiajun Shan et al.

Testing and Support Recovery in Population-Based Image Data

Jian Huang, Liuquan Sun, Lianqiang Qu et al.

Estimating Racial Disparities When Race is Not Observed

Cory McCartan, Robin Fisher, Jacob Goldin et al.

Integrated path stability selection

Omar Emlen Melikechi, Jeffrey W. Miller

Long-term effect estimation when combining clinical trial and observational follow-up datasets

Gang Cheng, Yen-Chi Chen, Joseph M. Unger et al.

Simulating diffusion bridges with score matching

J Heng, others

Abstract

Adaptation Using Spatially Distributed Gaussian Processes

Botond Szabo, Amine Hadji, Aad van der Vaart

Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, 2nd ed.

Xing Liu

Financial Data Analytics with R: Monte-Carlo Validation.

Tony Sit

Design-Based Uncertainty for Quasi-Experiments*

Ashesh Rambachan, Jonathan Roth

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials

B Ren, others

Abstract

Network Goodness-of-Fit for the Block-Model Family

Jiashun Jin, Zheng Tracy Ke, Jiajun Tang et al.

Correction to: Parameterizing and simulating from causal models

A family of toroidal diffusions with exact likelihood inference

E García-Portugués, M Sørensen

Abstract

Identifying and bounding the probability of necessity for causes of effects with ordinal outcomes

Chao Zhang, others

Abstract

Optimal clustering by Lloyd’s algorithm for low-rank mixture model

Dong Xia, Zhongyuan Lyu

Kernel density estimation with polyspherical data and its applications

Eduardo García-Portugués, Andrea Meilán-Vila

Debiasing Watermarks for Large Language Models via Maximal Coupling

Weijie Su, Yangxinyu Xie, Xiang Li et al.

Global and Episode-Specific Prediction of Recurrent Events Using Longitudinal Health Informatics Data

Chiung-Yu Huang, Yifei Sun, Sy Han Chiou

Aggregated Projection Method: A New Approach for Group Factor Model

Jiaqi Hu, Ting Li, Xueqin Wang

Bayesian Inference on Brain-Computer Interfaces via GLASS

Bangyao Zhao, Jane E. Huggins, Jian Kang

Analyzing Whale Calling through Hawkes Process Modeling

Bokgyeong Kang, Erin M. Schliep, Alan E. Gelfand et al.

Adaptive Selection for False Discovery Rate Control Leveraging Symmetry

Linglong Kong, Yuexin Chen, Kehan Wang et al.

Checking the Cox Proportional Hazards Model with Interval-Censored Data

Yangjianchen Xu, Donglin Zeng, D. Y. Lin

Bayesian Random-Effects Meta-Analysis Integrating Individual Participant Data and Aggregate Data

Yunxiang Huang, Hang J. Kim, Chiung-Yu Huang et al.

A Unified Framework for Residual Diagnostics in Generalized Linear Models and Beyond

Dungang Liu, Zewei Lin, Heping Zhang

High-dimensional covariance regression with application to co-expression QTL detection

Rakheon Kim, Jingfei Zhang

On a Notion of Graph Centrality Based on L1 Data Depth

Seungwoo Kang, Hee-Seok Oh

Higher-order accurate two-sample network inference and network hashing

Dong Xia, Meijia Shao, Yuan Zhang et al.

A unified generalization of the inverse regression methods via column selection

Yin Jin, Wei Luo

Identification and multiply robust estimation in causal mediation analysis across principal strata

Chao Cheng, Fan Li

Pseudo-likelihood Estimators for Graphical Models: Existence and Uniqueness

B Roycraft, B Rajaratnam

Abstract

Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions

Anders B. Kock, Rasmus S. Pedersen, Jesper R.-V. Sørensen

Goodness-of-fit tests for linear non-Gaussian structural equation models

D Schkoda, M Drton

Abstract

Unified Optimal Model Averaging with a General Loss Function based on Cross-Validation

Dalei Yu, Xinyu Zhang, Hua Liang

Who Are We Missing?: A Principled Approach to Characterizing the Underrepresented Population

Harsh Parikh, Rachael K. Ross, Elizabeth Stuart et al.

Ordinary differential equation models for a collection of discretized functions

Fang Yao, Lingxuan Shao

Nonparametric Test for Rough Volatility

Carsten H. Chong, Viktor Todorov

Estimating Heterogeneous Causal Mediation Effects with Bayesian Decision Tree Ensembles

Angela Ting, Antonio R. Linero

Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment

Kosuke Imai, Eli Ben-Michael, D. James Greiner et al.

Statistical Prediction and Machine Learning

Michal Pešta

A Smoothed-Bayesian Approach to Frequency Recovery from Sketched Data

Mario Beraha, Stefano Favaro, Matteo Sesia

Inference in Generalized Linear Models with Robustness to Misspecified Variances

Riccardo De Santis, Jelle J. Goeman, Jesse Hemerik et al.

Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment

Siyu Heng, Yang Feng, Jiawei Zhang

Communication-Efficient Distributed Estimation and Inference for Cox’s Model

Jianqing Fan, Pierre Bayle, Zhipeng Lou

Semiparametric localized principal stratification analysis with continuous strata

Yichi Zhang, Shu Yang

Testing Elliptical Models in High Dimensions

Siyao Wang, Miles E. Lopes

Least squares for cardinal paired comparisons data

Rahul Singh, others

Correction to: Convexity and measures of statistical association

A practical interval estimation method for spectral density function

Haihan Yu, Mark S. Kaiser, Daniel J. Nordman

A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning

Pan Zhao, Yifan Cui

Abstract

Proximal indirect comparison

Zehao Su, others

Abstract

Orthogonalized moment aberration for mixed-level multi-stratum factorial designs with partially-relaxed orthogonal block structures

Ming-Chung Chang

Multicalibration for Modeling Censored Survival Data with Universal Adaptability

Hanxuan Ye, Hongzhe Li

Abstract

Fair Coins Tend to Land on the Same Side They Started: Evidence from 350,757 Flips

František Bartoš, Alexandra Sarafoglou, Henrik R. Godmann et al.

Modelling tree survival for investigating climate change effects

Nicole Augustin, Axel Albrecht, Karim Anaya-Izquierdo et al.

Regularized halfspace depth for functional data

Hyemin Yeon, others

Incorporating Auxiliary Variables to Improve the Efficiency of Time-Varying Treatment Effect Estimation

Jieru Shi, Zhenke Wu, Walter Dempsey

Dependent Random Partitions by Shrinking Toward an Anchor

David B. Dahl, Richard L. Warr, Thomas P. Jensen

Structural restrictions in local causal discovery: identifying direct causes of a target variable

J Bodik, V Chavez-Demoulin

Abstract

Asymptotic Behavior of Adversarial Training Estimator underℓ∞-Perturbation

Yiling Xie, Xiaoming Huo

Likelihood Ratio Tests in Random Graph Models with Increasing Dimensions

Ting Yan, Ji Zhu, Yuanzhang Li et al.

Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling

Xiaotong Lin, others

Simultaneous Inference for Generalized Linear Models with Unmeasured Confounders

Jin-Hong Du, Kathryn Roeder, Larry Wasserman

Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes

Larry Wasserman, Jin-Hong Du, Zhenghao Zeng et al.

Nonsense associations in Markov random fields with pairwise dependence

Sohom Bhattacharya, others

Abstract

Posterior Predictive Design for Phase I Clinical Trials

Chenqi Fu, Shouhao Zhou, J. Jack Lee

A Bayesian Criterion for Rerandomization

Zhaoyang Liu, Tingxuan Han, Donald B. Rubin et al.

Distributional Off-Policy Evaluation in Reinforcement Learning

Zhengling Qi, Chenjia Bai, Zhaoran Wang et al.

Testing Mutually Exclusive Hypotheses for Multi-Response Regressions

Jiaqi Huang, Wenbiao Zhao, Lixing Zhu

Aggregating Dependent Signals with Heavy-Tailed Combination Tests

Lin Gui, others

Abstract

Robust functional principal component analysis for non-Euclidean random objects

Jiazhen Xu, others

Abstract

Detection and inference of changes in high-dimensional linear regression with nonsparse structures

Haeran Cho, others

Isotonic mechanism for exponential family estimation in machine learning peer review

Yuling Yan, others

Covariate-assisted bounds on causal effects with instrumental variables

Alexander W Levis, others

Improving the false coverage rate adjusted confidence intervals

Tzviel Frostig, Yoav Benjamini

Consistent and Scalable Composite Likelihood Estimation of Probit Models with Crossed Random Effects

R Bellio, others

Abstract

Estimation and Inference of Quantile Spatially Varying Coefficient Models Over Complicated Domains

Myungjin Kim, Li Wang, Huixia Judy Wang

Network-Based Neighborhood Regression

Jin-Hong Du, Yaoming Zhen

Hypothesis Testing for a Functional Parameter via Self-Normalization

Yi Zhang, Xiaofeng Shao

Distributed Tensor Principal Component Analysis with Data Heterogeneity

Xi Chen, Elynn Chen, Wenbo Jing et al.

Sparse Bayesian Multidimensional Item Response Theory

Jiguang Li, Robert Gibbons, Veronika Ročková

Tail calibration of probabilistic forecasts

Sam Allen, Jonathan Koh, Johan Segers et al.

Powerful Partial Conjunction Hypothesis Testing via Conditioning

B Liang, others

Abstract

An optimal design framework for lasso sign recovery

Jonathan W Stallrich, others

Bayesian mixture models with repulsive and attractive atoms

Mario Beraha, others

A statistical view of column subset selection

Anav Sood, Trevor Hastie

Unbiased and consistent nested sampling via sequential Monte Carlo

Robert Salomone, others

Prediction of Cognitive Function via Brain Region Volumes with Applications to Alzheimer’s Disease Based on Space-Factor-Guided Functional Principal Component Analysis

Shoudao Wen, Yi Li, Dehan Kong et al.

SymmPI: predictive inference for data with group symmetries

Edgar Dobriban, Mengxin Yu

Communication-Efficient Distributed Sparse Learning with Oracle Property and Geometric Convergence

Weidong Liu, Xiaojun Mao, Jiyuan Tu

Product centred Dirichlet processes for Bayesian multiview clustering

Alexander Dombowsky, David B Dunson

Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models

Tong Xu, others

Abstract

Bias correction of quadratic spectral estimators

Lachlan C Astfalck, others

Abstract

Statistical Inference for High-Dimensional Spectral Density Matrix

Jinyuan Chang, Xiaofeng Shao, Qing Jiang et al.

Data Fusion Using Weakly Aligned Sources

Sijia Li, Peter B. Gilbert, Rui Duan et al.

Augmented balancing weights as linear regression

David Bruns-Smith, others

Graphical methods for Order-of-Addition experiments

Nicholas Rios, Dennis K J Lin

Frequency Domain Statistical Inference for High-Dimensional Time Series

Jonas Krampe, Efstathios Paparoditis

Dynamic Regression of Longitudinal Trajectory Features

Huijuan Ma, Wei Zhao, John Hanfelt et al.

Cutting Feedback in Misspecified Copula Models

Michael Stanley Smith, Weichang Yu, David J. Nott et al.

Geodesic Mixed Effects Models for Repeatedly Observed/Longitudinal Random Objects

Hans-Georg Müller, Satarupa Bhattacharjee

Positive and Unlabeled Data: Model, Estimation, Inference, and Classification

Siyan Liu, Chi-Kuang Yeh, Xin Zhang et al.

An Economical Approach to Design Posterior Analyses

Luke Hagar, Nathaniel T. Stevens

Kernel Meets Sieve: Transformed Hazards Models with Sparse Longitudinal Covariates

Dayu Sun, Zhuowei Sun, Xingqiu Zhao et al.

Confidence on the focal: conformal prediction with selection-conditional coverage

Zhimei Ren, Ying Jin

Towards a turnkey approach for unbiased Monte Carlo estimation of smooth functions of expectations

Nicolas Chopin, others

Abstract

Convexity and measures of statistical association

Emanuele Borgonovo, others

A new approach to optimal design under model uncertainty motivated by multi-armed bandits

Mingyao Ai, Holger Dette, Zhengfu Liu et al.

Statistical Inference for High-Dimensional Convoluted Rank Regression

Liping Zhu, Leheng Cai, Xu Guo et al.

Degree-Heterogeneous Latent Class Analysis for High-Dimensional Discrete Data

Zhongyuan Lyu, Ling Chen, Yuqi Gu

Multi-Dimensional Domain Generalization with Low-Rank Structures

Sai Li, Linjun Zhang

Robustifying Likelihoods by Optimistically Re-weighting Data

Miheer Dewaskar, Christopher Tosh, Jeremias Knoblauch et al.

Network Varying Coefficient Model

Xinyan Fan, Kuangnan Fang, Chih-Ling Tsai et al.

Class-Specific Joint Feature Screening in Ultrahigh-Dimensional Mixture Regression

Kaili Jing, Abbas Khalili, Chen Xu

Efficient Estimation for Longitudinal Networks via Adaptive Merging

Haoran Zhang, Junhui Wang

Spatial Linear Models for Environmental Data.

Bruno Sansó

Distributional Outcome Regression via Quantile Functions and its Application to Modelling Continuously Monitored Heart Rate and Physical Activity

Rahul Ghosal, Sujit K. Ghosh, Jennifer A. Schrack et al.

Multi-resolution subsampling for linear classification with massive data

Haolin Chen, others

Fast Signal Region Detection With Application to Whole Genome Association Studies

Wei Zhang, Fang Yao, Fan Wang

Estimation of Over-Parameterized Models from an Auto-Modeling Perspective

Yiran Jiang, Chuanhai Liu

Phase-Type Distributions for Sieve Estimation

Xingqiu Zhao, Hu Xiangbin, Yudong Wang et al.

Deep Regression for Repeated Measurements

Fang Yao, Hang Zhou, Shunxing Yan

Estimating Heterogeneous Exposure Effects in the Case-Crossover Design Using BART

Jacob R. Englert, Stefanie T. Ebelt, Howard H. Chang

Sequential Monte Carlo testing by betting

Lasse Fischer, Aaditya Ramdas

High-Dimensional Variable Clustering based on Maxima of a Weakly Dependent Random Process

Alexis Boulin, Elena Di Bernardino, Thomas Laloë et al.

Correction to: Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods

A general framework for cutting feedback within modularized Bayesian inference

Yang Liu, Robert J B Goudie

Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models

Tate Jacobson

A spike-and-slab prior for dimension selection in generalized linear network eigenmodels

Joshua D Loyal, Yuguo Chen

Abstract

High-Dimensional Expected Shortfall Regression

Shushu Zhang, Kean Ming Tan, Wen-Xin Zhou et al.

Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Jue Hou, Tianxi Cai, Rui Duan et al.

Selecting informative conformal prediction sets with false coverage rate control

Ulysse Gazin, others

Hub Detection in Gaussian Graphical Models

José Á. Sánchez Gómez, Weibin Mo, Junlong Zhao et al.

Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling

Jinyuan Chang, others

Conformal prediction with conditional guarantees

Isaac Gibbs, others

U-Statistic Reduction: Higher-Order Accurate Risk Control and Statistical-Computational Trade-Off

Meijia Shao, Yuan Zhang, Dong Xia

A Novel Approach of High Dimensional Linear Hypothesis Testing Problem

Zhe Zhang, Xiufan Yu, Runze Li

An Adaptive Adjustment to theR2Statistic in High-Dimensional Elliptical Models

Shizhe Hong, Weiming Li, Qiang Liu et al.

Discovering the Network Granger Causality in Large Vector Autoregressive Models

Yoshimasa Uematsu, Takashi Yamagata

Identifying Genetic Variants for Brain Connectivity Using Ball Covariance Ranking and Aggregation

Heping Zhang, Wei Dai

High-Dimensional Knockoffs Inference for Time Series Data

Yingying Fan, Jinchi Lv, Chien-Ming Chi et al.

Inferences in Multinomial Dynamic Mixed Logit Models

Alwell Oyet, Brajendra C. Sutradhar, R. Prabhakar Rao

Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State

Donglin Zeng, D. Y. Lin, Yu Gu

A conditioning tactic that increases design sensitivity in observational block designs

Paul R Rosenbaum

Adaptive experiments toward learning treatment effect heterogeneity

Waverly Wei, others

High-dimensional Factor Analysis for Network-linked data

Jinming Li, others

Abstract

Semiparametric posterior corrections

Andrew Yiu, others

Random effects model-based sufficient dimension reduction for independent clustered data

Linh H. Nghiem, Francis K.C. Hui

Adaptive Testing for High-Dimensional Data

Xiaofeng Shao, Yangfan Zhang, Runmin Wang

Comparison of Longitudinal Trajectories Using a High-Dimensional Partial Linear Semiparametric Mixed-Effects Model

Sami Leon, Tong Tong Wu

Robust Inference for Federated Meta-Learning

Larry Han, Tianxi Cai, Zijian Guo et al.

Robust Bayesian Modeling of Counts with Zero Inflation and Outliers: Theoretical Robustness and Efficient Computation

Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa

Analysis of Variance of Tensor Product Reproducing Kernel Hilbert Spaces on Metric Spaces

Xueqin Wang, Zhanfeng Wang, Rui Pan et al.

Estimation and Inference for Nonparametric Expected Shortfall Regression over RKHS

Kean Ming Tan, Wen-Xin Zhou, Myeonghun Yu et al.

Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies

Dongxiao Wu, Xinran Li

Two-phase rejective sampling and its asymptotic properties

Peng Ding, Shu Yang

Large Precision Matrix Estimation with Unknown Group Structure

Cong Cheng, Yuan Ke, Wenyang Zhang

A Bias-Accuracy-Privacy Trilemma for Statistical Estimation

Gautam Kamath, Argyris Mouzakis, Matthew Regehr et al.

Augmentation invariant manifold learning

Shulei Wang

Estimation and Variable Selection for Interval-Censored Failure Time Data with Random Change Point and Application to Breast Cancer Study

Mingyue Du, Yichen Lou, Jianguo Sun