Found 135 papers
Sorted by: Newest FirstSpectrum-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...
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
Peter Bühlmann, Zhenyu Wang, Zijian Guo
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
Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference
Zhiling Gu, Shan Yu, Guannan Wang et al.
istributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
Zijian Guo, Peter Bühlmann, Zhenyu Wang
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...
“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...
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...
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 ...
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...
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...
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...
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 ...
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...
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...
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...
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...
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...
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...
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...
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...
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...
Chain-linked Multiple Matrix Integration via Embedding Alignment
Runbing Zheng, Minh Tang
Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning
Piyali Basak, Camille Maringe, F. Javier Rubio 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.
Inference on the proportion of variance explained in principal component analysis
Ronan Perry, Snigdha Panigrahi, Jacob Bien et al.
Information Theoretic Limits of Robust Sub-Gaussian Mean Estimation Under Star-Shaped Constraints
Matey Neykov, Akshay Prasadan
Communication-Efficient and Distributed-Oracle Estimation for High-Dimensional Quantile Regression
Songshan Yang, Yifan Gu, Xuming He et al.
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
Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
Kaizheng Wang
Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition
Andrea Montanari, Yuchen Wu
Solving the Poisson Equation Using Coupled Markov Chains
Pierre Etienne Jacob, Randal Douc, Anthony Lee et al.
Average Partial Effect Estimation Using Double Machine Learning
Harvey Klyne, Rajen Shah
High-Dimensional Hilbert-Schmidt Linear Regression with Hilbert Manifold Variables
Changwon Choi, Byeong U. Park
A Geometrical Analysis of Kernel Ridge Regression and its Applications
Zong Shang, Guillaume Lecué, Georgios Gavrilopoulos
A Flexible Defense Against the Winner’s Curse
William Fithian, Tijana Zrnic
Scalable Inference in Functional Linear Regression with Streaming Data
Linglong Kong, Jinhan Xie, Enze Shi et al.
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
The High-Dimensional Asymptotics of Principal Component Regression
Alden Green, Elad Romanov
Symmetry: A General Structure in Nonparametric Regression
Louis Goldwater Christie, John A. D. Aston
High-Dimensional Statistical Inference for Linkage Disequilibrium Score Regression and Its Cross-Ancestry Extensions
Fei Xue, Bingxin Zhao
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.
Debiased Regression Adjustment in Completely Randomized Experiments with Moderately High-Dimensional Covariates
Xin Lu, Fan Yang, Yuhao Wang
Algorithmic Stability Implies Training-Conditional Coverage for Distribution-Free Prediction Methods
Ruiting Liang, Rina Foygel Barber
On the Structural Dimension of Sliced Inverse Regression
Dongming Huang, Songtao Tian, Qian Lin
Asymptotically-Exact Selective Inference for Quantile Regression
Xuming He, Yumeng Wang, Snigdha Panigrahi
Near-Optimal Inference in Adaptive Linear Regression
Koulik Khamaru, Yash Deshpande, Tor Lattimore et al.
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 ...
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 ...
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....
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 ...
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...
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...
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...
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...
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...
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 ...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
Doubly robust conditional independence testing with generative neural networks
Yi Zhang, others
Estimating maximal symmetries of regression functions via subgroup lattices
Louis G Christie, John A D Aston
Conformal Prediction for Network-Assisted Regression
Robert Lunde, Elizaveta Levina, Ji Zhu
Factor pre-training in Bayesian multivariate logistic models
D B Dunson, L Mauri
Abstract
Higher Order Accurate Symmetric Bootstrap Confidence Intervals in High Dimensional Penalized Regression
Debraj Das, Arindam Chatterjee, S. N. Lahiri
The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
Jianqing Fan, Yuling Yan, Buxin Su et al.
Design-Based Uncertainty for Quasi-Experiments*
Ashesh Rambachan, Jonathan Roth
Bayesian Inference on Brain-Computer Interfaces via GLASS
Bangyao Zhao, Jane E. Huggins, Jian Kang
High-dimensional covariance regression with application to co-expression QTL detection
Rakheon Kim, Jingfei Zhang
A unified generalization of the inverse regression methods via column selection
Yin Jin, Wei Luo
Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions
Anders B. Kock, Rasmus S. Pedersen, Jesper R.-V. Sørensen
Statistical Prediction and Machine Learning
Michal Pešta
Least squares for cardinal paired comparisons data
Rahul Singh, others
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.
Asymptotic Behavior of Adversarial Training Estimator underℓ∞-Perturbation
Yiling Xie, Xiaoming Huo
Nonsense associations in Markov random fields with pairwise dependence
Sohom Bhattacharya, others
Abstract
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
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
Estimation and Inference of Quantile Spatially Varying Coefficient Models Over Complicated Domains
Myungjin Kim, Li Wang, Huixia Judy Wang
Tail calibration of probabilistic forecasts
Sam Allen, Jonathan Koh, Johan Segers et al.
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.
Augmented balancing weights as linear regression
David Bruns-Smith, others
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.
Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
Siyan Liu, Chi-Kuang Yeh, Xin Zhang et al.
Statistical Inference for High-Dimensional Convoluted Rank Regression
Liping Zhu, Leheng Cai, Xu Guo et al.
A new approach to optimal design under model uncertainty motivated by multi-armed bandits
Mingyao Ai, Holger Dette, Zhengfu Liu et al.
Multi-Dimensional Domain Generalization with Low-Rank Structures
Sai Li, Linjun Zhang
Class-Specific Joint Feature Screening in Ultrahigh-Dimensional Mixture Regression
Kaili Jing, Abbas Khalili, Chen Xu
Multi-resolution subsampling for linear classification with massive data
Haolin Chen, others
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.
Deep Regression for Repeated Measurements
Fang Yao, Hang Zhou, Shunxing Yan
High-Dimensional Expected Shortfall Regression
Shushu Zhang, Kean Ming Tan, Wen-Xin Zhou et al.
Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling
Jinyuan Chang, others
Identifying Genetic Variants for Brain Connectivity Using Ball Covariance Ranking and Aggregation
Heping Zhang, Wei Dai
Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State
Donglin Zeng, D. Y. Lin, Yu Gu
Estimation and Inference for Nonparametric Expected Shortfall Regression over RKHS
Kean Ming Tan, Wen-Xin Zhou, Myeonghun Yu et al.
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
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
Edgar Dobriban, Xinmeng Huang, Kan Xu et al.
Geometric Ergodicity of Trans-Dimensional Markov Chain Monte Carlo Algorithms
Qian Qin
Partial Quantile Tensor Regression
Limin Peng, Dayu Sun, Zhiping Qiu et al.
Local Signal Detection on Irregular Domains with Generalized Varying Coefficient Models
Annie Qu, Heng Lian, Chengzhu Zhang et al.
Coefficient Shape Alignment in Multiple Functional Linear Regression
Shuhao Jiao, Ngai-Hang Chan
‘On the behaviour of marginal and conditional AIC in linear mixed models’
Sonja Greven, Thomas Kneib