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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2,281 to 2,340 of 6,792

Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Xinkun Nie, Stefan Wager
stat.MLecon.EMmath.STarXiv:1712.04912v42017
Size-Independent Sample Complexity of Neural Networks
Noah Golowich, Alexander Rakhlin, Ohad Shamir
cs.LGcs.NEstat.MLarXiv:1712.06541v52017
Towards Accurate Binary Convolutional Neural Network
Xiaofan Lin, Cong Zhao, Wei Pan
cs.LGstat.MLarXiv:1711.11294v12017
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari
cs.AIcs.LGstat.MLarXiv:1711.07970v22017
Weakly-Supervised Neural Text Classification
Yu Meng, Jiaming Shen, Chao Zhang +1
cs.IRcs.CLcs.LGarXiv:1809.01478v22018
The Implicit Bias of Gradient Descent on Separable Data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson +2
stat.MLcs.LGarXiv:1710.10345v72017
Self-Normalizing Neural Networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr +1
cs.LGstat.MLarXiv:1706.02515v52017
A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski
cs.CVcs.AIcs.LGarXiv:1710.05381v22017
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang, Yixuan Li, R. Srikant
cs.LGstat.MLarXiv:1706.02690v52017
A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform
Thai T. Pham, Yuanyuan Shen
stat.MLcs.IRcs.LGarXiv:1706.02795v12017
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern +2
stat.MLcs.LGarXiv:1705.08292v22017
Fairness in Criminal Justice Risk Assessments: The State of the Art
Richard A. Berk, Hoda Heidari, Shahin Jabbari +2
stat.MLarXiv:1703.09207v22017
Minimax Regret Bounds for Reinforcement Learning
Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
stat.MLcs.AIcs.LGarXiv:1703.05449v22017
Tensor SVD: Statistical and Computational Limits
Anru Zhang, Dong Xia
math.STcs.LGstat.MEarXiv:1703.02724v42017
Simple, Efficient, and Neural Algorithms for Sparse Coding
Sanjeev Arora, Rong Ge, Tengyu Ma +1
cs.LGcs.DScs.NEarXiv:1503.00778v12015
Being Robust (in High Dimensions) Can Be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
cs.LGcs.DScs.ITarXiv:1703.00893v42017
Autoencoding Variational Inference For Topic Models
Akash Srivastava, Charles Sutton
stat.MLarXiv:1703.01488v12017
How to Escape Saddle Points Efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli +2
cs.LGmath.OCstat.MLarXiv:1703.00887v12017
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Sanjeev Arora, Rong Ge, Yingyu Liang +2
cs.LGcs.NEstat.MLarXiv:1703.00573v52017
A geometric analysis of subspace clustering with outliers
Mahdi Soltanolkotabi, Emmanuel J. Candés
cs.ITcs.LGmath.STarXiv:1112.4258v52011
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu +1
cs.AIcs.LGstat.MLarXiv:1702.08892v32017
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison, Andriy Mnih, Yee Whye Teh
cs.LGstat.MLarXiv:1611.00712v32016
TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
Léo Grinsztajn, Klemens Flöge, Oscar Key +23
cs.LGstat.MLarXiv:2511.08667v22025
Energy-based Generative Adversarial Network
Junbo Zhao, Michael Mathieu, Yann LeCun
cs.LGstat.MLarXiv:1609.03126v42016
Simulation-based optimal Bayesian experimental design for nonlinear systems
Xun Huan, Youssef M. Marzouk
stat.MLstat.COstat.MEarXiv:1108.4146v32011
Unifying Count-Based Exploration and Intrinsic Motivation
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski +3
cs.AIcs.LGstat.MLarXiv:1606.01868v22016
Adversarial Feature Learning
Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
cs.LGcs.AIcs.CVarXiv:1605.09782v72016
Fairness in Learning: Classic and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern +1
cs.LGstat.MLarXiv:1605.07139v22016
Deep Learning without Poor Local Minima
Kenji Kawaguchi
stat.MLcs.LGmath.OCarXiv:1605.07110v32016
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo +2
cs.LGstat.MLarXiv:1603.06560v42016
Bayesian Consensus Clustering
Eric F. Lock, David B. Dunson
stat.MLcs.LGarXiv:1302.7280v12013
Rényi Divergence Variational Inference
Yingzhen Li, Richard E. Turner
stat.MLcs.LGarXiv:1602.02311v32016
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
André F. T. Martins, Ramón Fernandez Astudillo
cs.CLcs.LGstat.MLarXiv:1602.02068v22016
Persistence Images: A Stable Vector Representation of Persistent Homology
Henry Adams, Sofya Chepushtanova, Tegan Emerson +7
cs.CGmath.ATstat.MLarXiv:1507.06217v32015
Gated Graph Sequence Neural Networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt +1
cs.LGcs.AIcs.NEarXiv:1511.05493v42015
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
Stephen H. Bach, Matthias Broecheler, Bert Huang +1
cs.LGcs.AIstat.MLarXiv:1505.04406v32015
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization
Xiangru Lian, Yijun Huang, Yuncheng Li +1
math.OCmath.NAstat.MLarXiv:1506.08272v52015
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition
Rong Ge, Furong Huang, Chi Jin +1
cs.LGmath.OCstat.MLarXiv:1503.02101v12015
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul +1
cs.SCcs.LGstat.MLarXiv:1502.05767v42015
Non-stochastic Best Arm Identification and Hyperparameter Optimization
Kevin Jamieson, Ameet Talwalkar
cs.LGstat.MLarXiv:1502.07943v12015
Deep Learning with Limited Numerical Precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan +1
cs.LGcs.NEstat.MLarXiv:1502.02551v12015
New insights and perspectives on the natural gradient method
James Martens
cs.LGstat.MLarXiv:1412.1193v112014
High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity
Po-Ling Loh, Martin J. Wainwright
math.STcs.ITstat.MLarXiv:1109.3714v42011
Generalized Low Rank Models
Madeleine Udell, Corinne Horn, Reza Zadeh +1
stat.MLcs.LGmath.OCarXiv:1410.0342v42014
Efficient Estimation of Mutual Information for Strongly Dependent Variables
Shuyang Gao, Greg Ver Steeg, Aram Galstyan
cs.ITphysics.data-anstat.MLarXiv:1411.2003v32014
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Yuchen Zhang, Lin Xiao
math.OCstat.MLarXiv:1409.3257v22014
On the Complexity of Best Arm Identification in Multi-Armed Bandit Models
Emilie Kaufmann, Olivier Cappé, Aurélien Garivier
stat.MLcs.LGarXiv:1407.4443v22014
Spectral redemption: clustering sparse networks
Florent Krzakala, Cristopher Moore, Elchanan Mossel +4
cs.SIcond-mat.stat-mechphysics.soc-pharXiv:1306.5550v22013
Minimizing Finite Sums with the Stochastic Average Gradient
Mark Schmidt, Nicolas Le Roux, Francis Bach
math.OCcs.LGstat.COarXiv:1309.2388v22013
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
Deanna Needell, Nathan Srebro, Rachel Ward
math.NAcs.CVcs.LGarXiv:1310.5715v52013
Phase Retrieval using Alternating Minimization
Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
stat.MLcs.ITcs.LGarXiv:1306.0160v22013
Tensor decompositions for learning latent variable models
Anima Anandkumar, Rong Ge, Daniel Hsu +2
cs.LGmath.NAstat.MLarXiv:1210.7559v42012
A Widely Applicable Bayesian Information Criterion
Sumio Watanabe
cs.LGstat.MLarXiv:1208.6338v12012
Sparse Approximation via Penalty Decomposition Methods
Zhaosong Lu, Yong Zhang
cs.LGmath.OCstat.COarXiv:1205.2334v22012
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
Alekh Agarwal, Sahand N. Negahban, Martin J. Wainwright
stat.MLcs.ITcs.LGarXiv:1102.4807v32011
Optimization with Sparsity-Inducing Penalties
Francis Bach, Rodolphe Jenatton, Julien Mairal +1
cs.LGmath.OCstat.MLarXiv:1108.0775v22011
Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
Eric F. Lock, Katherine A. Hoadley, J. S. Marron +1
stat.MLstat.APstat.MEarXiv:1102.4110v22011
Metamodel-based importance sampling for structural reliability analysis
V. Dubourg, F. Deheeger, B. Sudret
stat.MEstat.MLarXiv:1105.0562v22011
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
John Duchi, Alekh Agarwal, Martin Wainwright
math.OCeess.SYstat.MLarXiv:1005.2012v32010
Rumors in a Network: Who's the Culprit?
Devavrat Shah, Tauhid Zaman
stat.MLstat.AParXiv:0909.4370v22009

Machine Learning (stat)

Quasi-Oracle Estimation of Heterogeneous Treatment Effects

Size-Independent Sample Complexity of Neural Networks

Towards Accurate Binary Convolutional Neural Network

Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge

Weakly-Supervised Neural Text Classification

The Implicit Bias of Gradient Descent on Separable Data

Self-Normalizing Neural Networks

A systematic study of the class imbalance problem in convolutional neural networks

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform

The Marginal Value of Adaptive Gradient Methods in Machine Learning

Fairness in Criminal Justice Risk Assessments: The State of the Art

Minimax Regret Bounds for Reinforcement Learning

Tensor SVD: Statistical and Computational Limits

Simple, Efficient, and Neural Algorithms for Sparse Coding

Being Robust (in High Dimensions) Can Be Practical

Autoencoding Variational Inference For Topic Models

How to Escape Saddle Points Efficiently

Generalization and Equilibrium in Generative Adversarial Nets (GANs)

A geometric analysis of subspace clustering with outliers

Bridging the Gap Between Value and Policy Based Reinforcement Learning

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models

Energy-based Generative Adversarial Network

Simulation-based optimal Bayesian experimental design for nonlinear systems

Unifying Count-Based Exploration and Intrinsic Motivation

Adversarial Feature Learning

Fairness in Learning: Classic and Contextual Bandits

Deep Learning without Poor Local Minima

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

Bayesian Consensus Clustering

Rényi Divergence Variational Inference

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

Persistence Images: A Stable Vector Representation of Persistent Homology

Gated Graph Sequence Neural Networks

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization

Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition

Automatic differentiation in machine learning: a survey

Non-stochastic Best Arm Identification and Hyperparameter Optimization

Deep Learning with Limited Numerical Precision

New insights and perspectives on the natural gradient method

High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity

Generalized Low Rank Models

Efficient Estimation of Mutual Information for Strongly Dependent Variables

Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization

On the Complexity of Best Arm Identification in Multi-Armed Bandit Models

Spectral redemption: clustering sparse networks

Minimizing Finite Sums with the Stochastic Average Gradient

Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm

Phase Retrieval using Alternating Minimization

Tensor decompositions for learning latent variable models

A Widely Applicable Bayesian Information Criterion

Sparse Approximation via Penalty Decomposition Methods

Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions

Optimization with Sparsity-Inducing Penalties

Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Metamodel-based importance sampling for structural reliability analysis

Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling

Rumors in a Network: Who's the Culprit?