Found 181 papers
Sorted by: Newest FirstA novel statistical approach to analyze image classification
Juntong Chen, Sophie Langer, Johannes Schmidt-Hieber
Statistical Inference in Tensor Completion: Optimal Uncertainty Quantification and Statistical-to-Computational Gaps
Dong Xia, Wanteng Ma
Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin
Akshay Kumar, Jarvis Haupt
Recent works exploring the training dynamics of homogeneous neural network weights under gradient flow with small initialization have established that...
Fundamental Limits of Membership Inference Attacks on Machine Learning Models
Elisabeth Gassiat, Eric Aubinais, Pablo Piantanida
Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensitive informa...
Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction
Ying Cui, Jake Roth
Superquantiles have recently gained significant interest as a risk-aware metric for addressing fairness and distribution shifts in statistical learnin...
Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs
Dongsheng Ding, Kaiqing Zhang, Jiali Duan et al.
We study the sequential decision making problem of maximizing the expected total reward while satisfying a constraint on the expected total utility. ...
Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds
Nathanaël Munier, Emmanuel Soubies, Pierre Weiss
Given a forward mapping Φ : R^N → R^M and a point x* ∈ R^N , the region {x ∈ R^N , ||Φ(x) − Φ(x*)|| ≤ ε}, where ε ≥ 0 is a perturbation amplitude, rep...
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning
Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz et al.
Symmetry is present throughout nature and continues to play an increasingly central role in machine learning. In this paper, we provide a unifying the...
Stable learning using spiking neural networks equipped with affine encoders and decoders
A. Martina Neuman, Dominik Dold, Philipp Christian Petersen
We study the learning problem associated with spiking neural networks. Specifically, we focus on spiking neural networks composed of simple spiking ne...
Efficient Knowledge Deletion from Trained Models Through Layer-wise Partial Machine Unlearning
Vinay Chakravarthi Gogineni, Esmaeil S. Nadimi
Machine unlearning has garnered significant attention due to its ability to selectively erase knowledge obtained from specific training data samples i...
Hierarchical and Stochastic Crystallization Learning: Geometrically Leveraged Nonparametric Regression with Delaunay Triangulation
Guosheng Yin, Jiaqi Gu
High-dimensionality is known to be the bottleneck for both nonparametric regression and the Delaunay triangulation. To efficiently exploit the advanta...
Fair Text Classification via Transferable Representations
Thibaud Leteno, Michael Perrot, Charlotte Laclau et al.
Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g., women and men) remain...
Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
Kun Yuan, Tao Sun, Xinwang Liu
Gradient clipping has long been considered essential for ensuring the convergence of Stochastic Gradient Descent (SGD) in the presence of heavy-tailed...
Generalized multi-view model: Adaptive density estimation under low-rank constraints
Julien Chhor, Olga Klopp, Alexandre B. Tsybakov
We study the problem of bivariate discrete or continuous probability density estimation under low-rank constraints. For discrete distributions, we ass...
Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
Subhajit Patra, Sonali Panda, Bikram Keshari Parida et al.
Physics-informed neural networks have proven to be a powerful tool for solving differential equations, leveraging the principles of physics to inform ...
Graph-accelerated Markov Chain Monte Carlo using Approximate Samples
Leo L. Duan, Anirban Bhattacharya
It has become increasingly easy nowadays to collect approximate posterior samples via fast algorithms such as variational Bayes, but concerns exist ab...
Online Quantile Regression
Dong Xia, Wen-Xin Zhou, Yinan Shen
This paper addresses the challenge of integrating sequentially arriving data into the quantile regression framework, where the number of features may ...
On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference
Zhuangyan Fang, Ruiqi Zhao, Yue Liu et al.
Pairwise causal background knowledge about the existence or absence of causal edges and paths is frequently encountered in observational studies. Such...
Inferring Change Points in High-Dimensional Regression via Approximate Message Passing
Gabriel Arpino, Xiaoqi Liu, Julia Gontarek et al.
We consider the problem of localizing change points in a generalized linear model (GLM), a model that covers many widely studied problems in statistic...
Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime
Parthe Pandit, Zhichao Wang, Yizhe Zhu
Kernel ridge regression (KRR) is a popular class of machine learning models that has become an important tool for understanding deep learning. Much o...
Geometry and Stability of Supervised Learning Problems
Facundo Mémoli, Brantley Vose, Robert C. Williamson
We introduce a notion of distance between supervised learning problems, which we call the Risk distance. This distance, inspired by optimal transport,...
Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions
Qian Lin, Haobo Zhang, Yicheng Li et al.
Motivated by studies of neural networks, particularly the neural tangent kernel theory, we investigate the large-dimensional behavior of kernel ridge ...
A Hybrid Weighted Nearest Neighbour Classifier for Semi-Supervised Learning
Stephen M. S. Lee, Mehdi Soleymani
We propose a novel hybrid procedure for constructing a randomly weighted nearest neighbour classifier for semi-supervised learning. The procedure firs...
Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
Hao Liu, Jiahui Cheng, Wenjing Liao
Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its the...
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul
Given an intractable distribution $p$, the problem of variational inference (VI) is to find the best approximation from some more tractable family $Q$...
An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition
Qihang Lin, Negar Soheili, Runchao Ma et al.
Recent efforts to accelerate first-order methods have focused on convex optimization problems that satisfy a geometric property known as error-bound c...
Optimal subsampling for high-dimensional partially linear models via machine learning methods
Lei Wang, Heng Lian, Yujing Shao et al.
In this paper, we explore optimal subsampling strategies for estimating the parametric regression coefficients in partially linear models with unknown...
Decentralized Sparse Linear Regression via Gradient-Tracking
Ying Sun, Guang Cheng, Marie Maros et al.
We study sparse linear regression over a network of agents, modeled as an undirected graph without a center node. The estimation of the $s$-sparse ...
Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty
Yujin Jeong, Dominik Rothenhäusler
How can we draw trustworthy scientific conclusions? One criterion is that a study can be replicated by independent teams. While replication is critica...
Attainability of Two-Point Testing Rates for Finite-Sample Location Estimation
Spencer Compton, Gregory Valiant
A non-asymptotic distributional theory of approximate message passing for sparse and robust regression
Gen Li, Yuting Wei
A Two-step Estimating Approach for Heavy-tailed AR Models with Non-zero Median GARCH-type Noises
She Rui, Dai Linlin, Ling Shiqing
Uncertainty quantification for iterative algorithms in linear models with application to early stopping
Kai Tan, Pierre C Bellec
Adaptive Bayesian regression on data with low intrinsic dimensionality
Tao Tang, Xiuyuan Cheng, Nan Wu et al.
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy
Runze Li, Zhaoran Wang, Zhuoran Yang et al.
Random pairing MLE for estimation of item parameters in Rasch model
Yuepeng Yang, Cong Ma
Blessing from Human-AI Interaction: Super Policy Learning in Confounded Environments
Zhengling Qi, Jiayi Wang, Chengchun Shi
Fairness in Machine Learning: A Review for Statisticians
Xianwen He, Yao Li
Online Auction Design Using Distribution-Free Uncertainty Quantification with Applications to E-Commerce
Jiale Han, Xiaowu Dai
Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraintsGet access
Arnab Auddyand others
Finite- and large-sample inference for model and coefficients in high-dimensional linear regression with repro samples
Linjun Zhang, Peng Wang, Minge Xie
Identification and estimation of interaction effects in nonparametric additive regressionGet access
Seung Hyun Moonand others
Spatial self-confounding: Smoothness-related estimation bias in spatial regression models
David BolinandJonas Wallin
Regression graphs and sparsity-inducing reparametrizations
J Rybakand others
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...
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
Peter Bühlmann, Zijian Guo, Zhenyu Wang
Transfer learning under large-scale low-rank regression models
Hongyu Zhao, Seyoung Park, Eun Ryung Lee et al.
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm
Linjun Zhang, Zhanrui Cai, Xintao Xia
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
Peter Bühlmann, Zijian Guo, 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
Xueqin Wang, Peng Chen, Jin Zhu 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
Zuofeng Shang, Tianyang Hu, Ruiqi Liu 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
Ismaël Castillo, Paul Egels
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, Jian Kang, Ben Wu
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
Antonio R. Linero, Piyali Basak, Camille Maringe 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
Snigdha Panigrahi, Ronan Perry, 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
Xuming He, Songshan Yang, Yifan Gu 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
Jingyang Li, Kuangyu Ding, 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 modelsGet access
L MauriandD B Dunson
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 dependenceGet access
Sohom Bhattacharyaand others
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
Dynamic Regression of Longitudinal Trajectory Features
Huijuan Ma, Wei Zhao, John Hanfelt et al.
Frequency Domain Statistical Inference for High-Dimensional Time Series
Jonas Krampe, Efstathios Paparoditis
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.
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
A new approach to optimal design under model uncertainty motivated by multi-armed bandits
Mingyao Ai, Holger Dette, Zhengfu Liu et al.
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
Deep Regression for Repeated Measurements
Fang Yao, Hang Zhou, Shunxing Yan
High-Dimensional Expected Shortfall Regression
Xuming He, 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
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