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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8,581 to 8,640 of 9,676

GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying, Dylan Bourgeois, Jiaxuan You +2
cs.LGstat.MLarXiv:1903.03894v42019
Neural Variational Inference and Learning in Belief Networks
Andriy Mnih, Karol Gregor
cs.LGstat.MLarXiv:1402.0030v22014
Structured Graph Learning for Clustering and Semi-supervised Classification
Zhao Kang, Chong Peng, Qiang Cheng +4
cs.LGcs.AIcs.CVarXiv:2008.13429v12020
Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent
Mehdi Abbana Bennani, Thang Doan, Masashi Sugiyama
stat.MLcs.LGarXiv:2006.11942v42020
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin
cs.LGstat.MLarXiv:1705.08841v12017
A Characteristic Function Approach to Deep Implicit Generative Modeling
Abdul Fatir Ansari, Jonathan Scarlett, Harold Soh
cs.LGcs.NEstat.MLarXiv:1909.07425v22019
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach
math.OCcs.LGstat.MLarXiv:1602.05419v22016
A positive resolution of the gap-entropy conjecture
P. M. Aronow, Nathan Kallus, Patrick Lopatto
cs.LGstat.MLarXiv:2609.10529v12026
Rumors in a Network: Who's the Culprit?
Devavrat Shah, Tauhid Zaman
stat.MLstat.AParXiv:0909.4370v22009
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Tom Zahavy, Matan Haroush, Nadav Merlis +2
cs.LGstat.MLarXiv:1809.02121v32018
Do Deep Generative Models Know What They Don't Know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh +2
stat.MLcs.LGarXiv:1810.09136v32018
Convergence of Muon with Newton-Schulz
Gyu Yeol Kim, Min-hwan Oh
stat.MLcs.LGmath.OCarXiv:2601.19156v12026
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Michela Paganini, Luke de Oliveira, Benjamin Nachman
hep-excs.LGhep-pharXiv:1712.10321v12017
Verifying Individual Fairness in Machine Learning Models
Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha
cs.LGcs.AIstat.MLarXiv:2006.11737v12020
Structured Pruning of Large Language Models
Ziheng Wang, Jeremy Wohlwend, Tao Lei
cs.CLcs.LGstat.MLarXiv:1910.04732v22019
Towards Optimal Power Control via Ensembling Deep Neural Networks
Fei Liang, Cong Shen, Wei Yu +1
eess.SPcs.ITstat.MLarXiv:1807.10025v22018
On the Effectiveness of Least Squares Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie +3
cs.CVcs.LGstat.MLarXiv:1712.06391v22017
Generative hypergraph clustering: from blockmodels to modularity
Philip S. Chodrow, Nate Veldt, Austin R. Benson
cs.SIcs.DMphysics.data-anarXiv:2101.09611v42021
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Anastasios N Angelopoulos, Amit P Kohli, Stephen Bates +5
cs.LGcs.CVeess.IVarXiv:2202.05265v12022
What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman +1
cs.LGstat.MLarXiv:2104.14421v12021
Federated Learning Meets Multi-objective Optimization
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang +1
cs.LGstat.MLarXiv:2006.11489v22020
Distributed Clustering of Linear Bandits in Peer to Peer Networks
Nathan Korda, Balazs Szorenyi, Shuai Li
cs.LGcs.AIstat.MLarXiv:1604.07706v32016
Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents
Nadeem Shaikh
cs.LGcs.AIstat.MLarXiv:2608.24087v12026
Heterogeneous Graph Neural Networks for Malicious Account Detection
Ziqi Liu, Chaochao Chen, Xinxing Yang +3
cs.LGcs.CRstat.MLarXiv:2002.12307v12020
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Zeyuan Allen-Zhu, Yang Yuan
cs.LGcs.DSmath.OCarXiv:1506.01972v32015
What graph neural networks cannot learn: depth vs width
Andreas Loukas
cs.LGstat.MLarXiv:1907.03199v22019
Wasserstein Auto-Encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly +1
stat.MLcs.LGarXiv:1711.01558v42017
Entangled Watermarks as a Defense against Model Extraction
Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran +1
cs.CRstat.MLarXiv:2002.12200v22020
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu +1
cs.AIcs.LGstat.MLarXiv:1702.08892v32017
Probabilistic Prediction of Vehicle Semantic Intention and Motion
Yeping Hu, Wei Zhan, Masayoshi Tomizuka
cs.LGstat.MLarXiv:1804.03629v12018
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah
stat.MLcs.CYcs.LGarXiv:2007.10306v32020
Optimization of Molecules via Deep Reinforcement Learning
Zhenpeng Zhou, Steven Kearnes, Li Li +2
cs.LGcs.AIstat.MLarXiv:1810.08678v32018
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
Lei Chen, Le Wu, Richang Hong +2
cs.IRcs.LGstat.MLarXiv:2001.10167v12020
Fine-grained Sentiment Classification using BERT
Manish Munikar, Sushil Shakya, Aakash Shrestha
cs.CLcs.LGstat.MLarXiv:1910.03474v12019
Bike Flow Prediction with Multi-Graph Convolutional Networks
Di Chai, Leye Wang, Qiang Yang
cs.LGcs.AIstat.MLarXiv:1807.10934v12018
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
Trapit Bansal, David Belanger, Andrew McCallum
stat.MLcs.CLcs.LGarXiv:1609.02116v22016
Pseudo-likelihood methods for community detection in large sparse networks
Arash A. Amini, Aiyou Chen, Peter J. Bickel +1
cs.SIcs.LGmath.STarXiv:1207.2340v32012
The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs
Pengrui Han, Rafal Kocielnik, Peiyang Song +4
cs.AIcs.CLcs.CYarXiv:2509.03730v22025
Supervising strong learners by amplifying weak experts
Paul Christiano, Buck Shlegeris, Dario Amodei
cs.LGcs.AIstat.MLarXiv:1810.08575v12018
Neural Ordinary Differential Equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt +1
cs.LGcs.AIstat.MLarXiv:1806.07366v52018
A review of machine learning applications in wildfire science and management
Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian +3
cs.LGstat.MLarXiv:2003.00646v22020
Additive Nonparametric Regression with Spatial and Network Objects
Rajarshi Guhaniyogi, Pritam Dey, Krishnendu Chandra +2
stat.MEstat.APstat.MLarXiv:2609.26867v12026
Exact Dynamics and Finite-Sample Trajectory Recovery of Linear Recursive Feature Machines
Andrew Cheng, Bobak T. Kiani, Yue M. Lu +1
cs.LGstat.MLarXiv:2610.09196v12026
Getting a CLUE: A Method for Explaining Uncertainty Estimates
Javier Antorán, Umang Bhatt, Tameem Adel +2
stat.MLcs.LGarXiv:2006.06848v22020
Maximum Likelihood Training of Score-Based Diffusion Models
Yang Song, Conor Durkan, Iain Murray +1
stat.MLcs.LGarXiv:2101.09258v42021
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Zonghan Wu, Shirui Pan, Guodong Long +3
cs.LGstat.MLarXiv:2005.11650v12020
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur
stat.MLcs.LGarXiv:1210.1190v12012
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang +2
cs.LGmath.OCstat.MLarXiv:2003.00534v22020
Learning Equations for Extrapolation and Control
Subham S. Sahoo, Christoph H. Lampert, Georg Martius
cs.LGstat.MLarXiv:1806.07259v12018
Multi-Agent Generative Adversarial Imitation Learning
Jiaming Song, Hongyu Ren, Dorsa Sadigh +1
cs.LGcs.AIcs.MAarXiv:1807.09936v12018
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Bernd Bischl, Martin Binder, Michel Lang +9
stat.MLcs.LGarXiv:2107.05847v32021
Semantic Human Matting
Quan Chen, Tiezheng Ge, Yanyu Xu +3
cs.CVcs.GRcs.LGarXiv:1809.01354v22018
Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques
Sujith Mangalathu, Jong-Su Jeon
cs.CEcs.LGstat.MLarXiv:1807.09761v12018
Corralling a Band of Bandit Algorithms
Alekh Agarwal, Haipeng Luo, Behnam Neyshabur +1
cs.LGstat.MLarXiv:1612.06246v32016
Implicit Semantic Data Augmentation for Deep Networks
Yulin Wang, Xuran Pan, Shiji Song +3
cs.CVcs.LGstat.MLarXiv:1909.12220v52019
Adversarial Perturbations Fool Deepfake Detectors
Apurva Gandhi, Shomik Jain
cs.CVcs.LGcs.NEarXiv:2003.10596v22020
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen +1
cs.LGcs.AIcs.CGarXiv:2104.13478v22021
Fundamental Limits of Weak Recovery with Applications to Phase Retrieval
Marco Mondelli, Andrea Montanari
stat.MLcs.ITmath.ITarXiv:1708.05932v32017
Deep Signature Transforms
Patric Bonnier, Patrick Kidger, Imanol Perez Arribas +2
cs.LGstat.MLarXiv:1905.08494v22019
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann, Tor Lattimore, Emma Brunskill
cs.LGcs.AIstat.MLarXiv:1703.07710v32017

Machine Learning (stat)

GNNExplainer: Generating Explanations for Graph Neural Networks

Neural Variational Inference and Learning in Belief Networks

Structured Graph Learning for Clustering and Semi-supervised Classification

Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent

Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

A Characteristic Function Approach to Deep Implicit Generative Modeling

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

A positive resolution of the gap-entropy conjecture

Rumors in a Network: Who's the Culprit?

Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

Do Deep Generative Models Know What They Don't Know?

Convergence of Muon with Newton-Schulz

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

Verifying Individual Fairness in Machine Learning Models

Structured Pruning of Large Language Models

Towards Optimal Power Control via Ensembling Deep Neural Networks

On the Effectiveness of Least Squares Generative Adversarial Networks

Generative hypergraph clustering: from blockmodels to modularity

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

What Are Bayesian Neural Network Posteriors Really Like?

Federated Learning Meets Multi-objective Optimization

Distributed Clustering of Linear Bandits in Peer to Peer Networks

Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents

Heterogeneous Graph Neural Networks for Malicious Account Detection

Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

What graph neural networks cannot learn: depth vs width

Wasserstein Auto-Encoders

Entangled Watermarks as a Defense against Model Extraction

Bridging the Gap Between Value and Policy Based Reinforcement Learning

Probabilistic Prediction of Vehicle Semantic Intention and Motion

An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction

Optimization of Molecules via Deep Reinforcement Learning

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Fine-grained Sentiment Classification using BERT

Bike Flow Prediction with Multi-Graph Convolutional Networks

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

Pseudo-likelihood methods for community detection in large sparse networks

The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs

Supervising strong learners by amplifying weak experts

Neural Ordinary Differential Equations

A review of machine learning applications in wildfire science and management

Additive Nonparametric Regression with Spatial and Network Objects

Exact Dynamics and Finite-Sample Trajectory Recovery of Linear Recursive Feature Machines

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Maximum Likelihood Training of Score-Based Diffusion Models

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization

Provably Efficient Safe Exploration via Primal-Dual Policy Optimization

Learning Equations for Extrapolation and Control

Multi-Agent Generative Adversarial Imitation Learning

Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges

Semantic Human Matting

Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques

Corralling a Band of Bandit Algorithms

Implicit Semantic Data Augmentation for Deep Networks

Adversarial Perturbations Fool Deepfake Detectors

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Fundamental Limits of Weak Recovery with Applications to Phase Retrieval

Deep Signature Transforms

Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning