Machine Learning (stat)

Papers filed under stat.ML on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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1,981 to 2,040 of 6,772

Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan, J. Zico Kolter
cs.LGstat.MLarXiv:1902.04742v42019
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
cs.LGcs.DCcs.DSarXiv:1902.00340v12019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich +1
cs.LGmath.OCstat.MLarXiv:1901.09847v22019
Unsupervised Learning via Meta-Learning
Kyle Hsu, Sergey Levine, Chelsea Finn
cs.LGcs.AIcs.CVarXiv:1810.02334v62018
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec +1
cs.LGcs.CVstat.MLarXiv:1810.00826v32018
Actionable Recourse in Linear Classification
Berk Ustun, Alexander Spangher, Yang Liu
stat.MLcs.LGarXiv:1809.06514v22018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
Cong Fang, Chris Junchi Li, Zhouchen Lin +1
math.OCcs.LGstat.MLarXiv:1807.01695v22018
Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Maxim Neumann +7
cs.CVcs.LGstat.MLarXiv:1712.00559v32017
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +1
stat.MLcs.LGarXiv:1610.08452v22016
Stealing Machine Learning Models via Prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels +2
cs.CRcs.LGstat.MLarXiv:1609.02943v22016
Data Programming: Creating Large Training Sets, Quickly
Alexander Ratner, Christopher De Sa, Sen Wu +2
stat.MLcs.AIcs.LGarXiv:1605.07723v32016
Convex Optimization: Algorithms and Complexity
Sébastien Bubeck
math.OCcs.CCcs.LGarXiv:1405.4980v22014
Tuned Models of Peer Assessment in MOOCs
Chris Piech, Jonathan Huang, Zhenghao Chen +3
cs.LGcs.AIcs.HCarXiv:1307.2579v12013
Thompson Sampling for Contextual Bandits with Linear Payoffs
Shipra Agrawal, Navin Goyal
cs.LGcs.DSstat.MLarXiv:1209.3352v42012
Thompson Sampling: An Asymptotically Optimal Finite Time Analysis
Emilie Kaufmann, Nathaniel Korda, Rémi Munos
stat.MLcs.LGarXiv:1205.4217v22012
Confidence-Aware Learning for Deep Neural Networks
Jooyoung Moon, Jihyo Kim, Younghak Shin +1
cs.LGstat.MLarXiv:2007.01458v32020
Analysis of a Random Forests Model
Gérard Biau
stat.MLmath.STarXiv:1005.0208v32010
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra +2
cs.LGstat.MLarXiv:1506.06840v22015
Bayes-Optimal BER and AUC: Estimation and Evaluation of Estimators
Ryota Ushio, Takashi Ishida, Masashi Sugiyama
cs.LGstat.MLarXiv:2609.02304v12026
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Juncheng Li, Frank R. Schmidt, J. Zico Kolter
cs.CVcs.CRcs.LGarXiv:1904.00759v42019
Functional linear regression from sparse to dense designs: a pooling-ridge method and minimax optimality
Shunxing Yan, Fang Yao
stat.MEcs.LGmath.STarXiv:2608.25468v12026
Barycentric Weak Inner-Product Gromov-Wasserstein
Youssef Mroueh
math.OCstat.MLarXiv:2608.25145v12026
Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features
Xiaoyan Li, Shixin Xu, Arvind Gupta +1
cs.CVstat.MLarXiv:2608.24723v12026
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Sham Kakade, Adam Tauman Kalai, Varun Kanade +1
cs.AIcs.LGstat.MLarXiv:1104.2018v12011
Primal--Dual Alternating Neural Learning for Timely Classification with Performance Guarantees
Jiaming Qiu, Yingye Zheng, Ying-Qi Zhao
stat.MLcs.LGstat.MEarXiv:2608.23480v12026
Symbolic Neural ODEs: Learning interpretable models from time-series data
Nibodh Boddupalli, Jeff Moehlis
cs.LGeess.SYmath.DSarXiv:2608.22112v12026
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
Indro Spinelli, Simone Scardapane, Aurelio Uncini
cs.LGstat.MLarXiv:1905.01907v22019
Random Hazard Forests
Hemant Ishwaran, Eileen M. Hsich, Udaya B. Kogalur +1
stat.MLcs.LGstat.MEarXiv:2608.21597v12026
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
cs.LGcs.AIstat.MLarXiv:1906.03393v42019
Deep Neural Decision Trees
Yongxin Yang, Irene Garcia Morillo, Timothy M. Hospedales
cs.LGstat.MLarXiv:1806.06988v12018
Policy Optimization and Statistical Inference for Online Contextual Matrix Games
Liner Xiang, Yixin Wang, Hengrui Cai
stat.MLcs.LGmath.STarXiv:2608.17173v12026
Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
Shuai Huang, Zhe Qu, Zhaowei Hua +3
stat.MLcs.LGstat.AParXiv:2608.16864v12026
Sparse Prototype Code Underlies Classification and Prediction Across Modalities
Yehonatan Avidan, Daniel D. Lee, Haim Sompolinsky
cs.LGcond-mat.dis-nncs.AIarXiv:2608.15632v12026
Offline Deep Q* Estimation with Diffusion Models
Xiaohong Chen, Yuling Jiao, Lican Kang +2
stat.MLcs.LGarXiv:2608.14401v12026
The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow
Raphaël Berthier
cs.LGmath.OCstat.MLarXiv:2609.01034v12026
Learning Certifiably Optimal Rule Lists for Categorical Data
Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi +2
stat.MLcs.LGarXiv:1704.01701v42017
Efficiently Sampling Functions from Gaussian Process Posteriors
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin +2
stat.MLcs.LGstat.COarXiv:2002.09309v42020
Revisiting Energy-based Tabular Anomaly Detection: Energy and Reconstruction are Complementary
Junichiro Niimi
cs.LGstat.MLarXiv:2608.14186v12026
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
Cédric Févotte, Nicolas Dobigeon
stat.MEstat.MLarXiv:1401.5649v22014
Tight Complexity Bounds for Optimizing Composite Objectives
Blake Woodworth, Nathan Srebro
math.OCcs.LGstat.MLarXiv:1605.08003v32016
A deep learning model for estimating story points
Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran +3
cs.SEcs.LGstat.MLarXiv:1609.00489v22016
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Ziwei Ji, Matus Telgarsky
cs.LGmath.OCstat.MLarXiv:1909.12292v42019
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
Kimin Lee, Kibok Lee, Jinwoo Shin +1
cs.LGstat.MLarXiv:1910.05396v32019
CEM-RL: Combining evolutionary and gradient-based methods for policy search
Aloïs Pourchot, Olivier Sigaud
cs.LGcs.NEstat.MLarXiv:1810.01222v32018
When Many Answers Are Valid, Voting Fails: Symbolic Verification for Best-of-K Causal Reasoning in LLMs
Omatharv Bharat Vaidya, Connor Thomas Jerzak, Zayne Rea Sprague +2
cs.AIstat.MLarXiv:2608.03506v12026
Neural Networks Provably Learn Spectral Representations for Group Composition
Jianliang He, Leda Wang, Fengzhuo Zhang +2
cs.LGmath.OCmath.RTarXiv:2606.02993v22026
Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation
Samson Gourevitch, Yazid Janati, Dario Shariatian +4
cs.LGstat.MLarXiv:2605.22765v12026
SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction
Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov, Hafize Gonca Cömert
cs.LGecon.EMstat.MLarXiv:2605.19014v12026
From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion
Satoshi Hayakawa
cs.LGcs.AImath.PRarXiv:2609.01043v12026
Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD
Emiel Hoogeboom, David Ruhe, Jonathan Heek +2
cs.LGcs.CVstat.MLarXiv:2603.20155v12026
Remasking Discrete Diffusion Models with Inference-Time Scaling
Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo +1
cs.LGstat.MLarXiv:2503.00307v42025
SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss +1
cs.LGcs.AIstat.MLarXiv:2603.05483v12026
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
Alekh Agarwal, Sham Kakade, Lin F. Yang
cs.LGmath.PRstat.MLarXiv:1906.03804v32019
On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking
Jianliang He, Leda Wang, Siyu Chen +1
cs.LGmath.OCstat.MLarXiv:2602.16849v12026
Introduction to Machine Learning
Laurent Younes
stat.MLcs.LGarXiv:2409.02668v22024
Momentum Improves Normalized SGD
Ashok Cutkosky, Harsh Mehta
cs.LGmath.OCstat.MLarXiv:2002.03305v22020
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
Vitaly Feldman, Audra McMillan, Kunal Talwar
cs.LGcs.CRcs.DSarXiv:2012.12803v32020
Towards Understanding Sycophancy in Language Models
Mrinank Sharma, Meg Tong, Tomasz Korbak +16
cs.CLcs.AIcs.LGarXiv:2310.13548v42023
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao +6
cs.LGcs.AIcs.CVarXiv:2310.02279v32023
Consistency Models
Yang Song, Prafulla Dhariwal, Mark Chen +1
cs.LGcs.CVstat.MLarXiv:2303.01469v22023

Machine Learning (stat)

Uniform convergence may be unable to explain generalization in deep learning

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Unsupervised Learning via Meta-Learning

How Powerful are Graph Neural Networks?

Actionable Recourse in Linear Classification

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator

Progressive Neural Architecture Search

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Stealing Machine Learning Models via Prediction APIs

Data Programming: Creating Large Training Sets, Quickly

Convex Optimization: Algorithms and Complexity

Tuned Models of Peer Assessment in MOOCs

Thompson Sampling for Contextual Bandits with Linear Payoffs

Thompson Sampling: An Asymptotically Optimal Finite Time Analysis

Confidence-Aware Learning for Deep Neural Networks

Analysis of a Random Forests Model

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants

Bayes-Optimal BER and AUC: Estimation and Evaluation of Estimators

Adversarial camera stickers: A physical camera-based attack on deep learning systems

Functional linear regression from sparse to dense designs: a pooling-ridge method and minimax optimality

Barycentric Weak Inner-Product Gromov-Wasserstein

Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features

Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression

Primal--Dual Alternating Neural Learning for Timely Classification with Performance Guarantees

Symbolic Neural ODEs: Learning interpretable models from time-series data

Missing Data Imputation with Adversarially-trained Graph Convolutional Networks

Random Hazard Forests

Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling

Deep Neural Decision Trees

Policy Optimization and Statistical Inference for Online Contextual Matrix Games

Non-Crossing Deep Quantile Regression for Distributional Survival Prediction

Sparse Prototype Code Underlies Classification and Prediction Across Modalities

Offline Deep Q* Estimation with Diffusion Models

The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow

Learning Certifiably Optimal Rule Lists for Categorical Data

Efficiently Sampling Functions from Gaussian Process Posteriors

Revisiting Energy-based Tabular Anomaly Detection: Energy and Reconstruction are Complementary

Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization

Tight Complexity Bounds for Optimizing Composite Objectives

A deep learning model for estimating story points

Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

CEM-RL: Combining evolutionary and gradient-based methods for policy search

When Many Answers Are Valid, Voting Fails: Symbolic Verification for Best-of-K Causal Reasoning in LLMs

Neural Networks Provably Learn Spectral Representations for Group Composition

Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation

SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction

From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion

Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD

Remasking Discrete Diffusion Models with Inference-Time Scaling

SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis

Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal

On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking

Introduction to Machine Learning

Momentum Improves Normalized SGD

Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling

Towards Understanding Sycophancy in Language Models

Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

Consistency Models