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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8,401 to 8,460 of 9,641

Momentum in large-batch training: Polyak enlarges the critical batch size, Nesterov improves data efficiency
Jia-Nan Wang, Zixun Huang, Kairui Li +1
stat.MLcs.LGmath.OCarXiv:2609.02728v12026
Is Homophily a Necessity for Graph Neural Networks?
Yao Ma, Xiaorui Liu, Neil Shah +1
cs.LGstat.MLarXiv:2106.06134v42021
Compositionality decomposed: how do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul +1
cs.CLcs.AIcs.LGarXiv:1908.08351v22019
A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding +1
cs.IRcs.LGstat.MLarXiv:1605.09477v12016
Quickest Change Detection with Diffusion-Integrated Scores
Arman Adibi, Mohammadreza Maleki, Sanjeev Kulkarni +1
stat.MLcs.LGarXiv:2610.12200v12026
A Distributional Perspective on Reinforcement Learning
Marc G. Bellemare, Will Dabney, Rémi Munos
cs.LGcs.AIstat.MLarXiv:1707.06887v12017
Dense Structural Compression of Transformers via Gauge-Correct Channel Removal
Jed A. Duersch, Naïm Es-Sebbani, Nathanaël Haas +1
cs.LGstat.MLarXiv:2609.07264v12026
Iterative Machine Teaching
Weiyang Liu, Bo Dai, Ahmad Humayun +5
stat.MLcs.LGarXiv:1705.10470v32017
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler +1
cs.LGstat.MLarXiv:1905.05934v12019
A nonlocal physics-informed deep learning framework using the peridynamic differential operator
Ehsan Haghighat, Ali Can Bekar, Erdogan Madenci +1
cs.LGstat.MLarXiv:2006.00446v12020
Kronecker Graphs: An Approach to Modeling Networks
Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg +2
stat.MLcs.DSphysics.data-anarXiv:0812.4905v22008
The Expressive Power of Low-Rank Adaptation
Yuchen Zeng, Kangwook Lee
cs.LGcs.AIcs.CLarXiv:2310.17513v32023
Optimal random quantisers for spherically symmetric distributions
Luc Pronzato, Anatoly Zhigljavsky
math.STcs.LGstat.MLarXiv:2610.11772v12026
Understanding and Robustifying Differentiable Architecture Search
Arber Zela, Thomas Elsken, Tonmoy Saikia +3
cs.LGcs.AIcs.CVarXiv:1909.09656v22019
Low Data Drug Discovery with One-shot Learning
Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu +1
cs.LGstat.MLarXiv:1611.03199v12016
Hypothesis Testing for Automated Community Detection in Networks
Peter J. Bickel, Purnamrita Sarkar
stat.MLcs.LGcs.SIarXiv:1311.2694v22013
Nonparametric ridge estimation
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli +1
math.STcs.LGstat.MLarXiv:1212.5156v32012
Finite-Sample Analysis of Proximal Gradient TD Algorithms
Bo Liu, Ji Liu, Mohammad Ghavamzadeh +2
cs.LGstat.MLarXiv:2006.14364v22020
A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network
Seyoung Kim, Kyung-Ah Sohn, Eric P. Xing
stat.MLq-bio.GNq-bio.MNarXiv:0811.2026v12008
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
Yulong Cao, Chaowei Xiao, Dawei Yang +4
cs.CRcs.CVcs.LGarXiv:1907.05418v12019
An Accurate and Single-Communication Federated Inference Algorithm
Laura Montagnani, Anthony CC Coolen, Marianne A Jonker
stat.MEmath.STstat.COarXiv:2608.27063v12026
Fast Two-Sample Testing with Analytic Representations of Probability Measures
Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic +1
stat.MLarXiv:1506.04725v12015
Sharp Bounds on the Approximation Rates, Metric Entropy, and $n$-widths of Shallow Neural Networks
Jonathan W. Siegel, Jinchao Xu
stat.MLcs.ITcs.LGarXiv:2101.12365v102021
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
Shengyi Huang, Santiago Ontañón
cs.LGcs.AIstat.MLarXiv:2006.14171v32020
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
Guy Revach, Nir Shlezinger, Xiaoyong Ni +3
eess.SPcs.LGstat.MLarXiv:2107.10043v32021
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg +22
stat.MLcs.CLcs.MSarXiv:1701.03980v12017
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi +1
cs.LGcs.AIcs.CVarXiv:1604.06057v22016
Random Vector Functional Link Networks for Function Approximation on Manifolds
Deanna Needell, Aaron A. Nelson, Rayan Saab +2
stat.MLcs.ITcs.LGarXiv:2007.15776v42020
On the Distribution of Penalized Maximum Likelihood Estimators: The LASSO, SCAD, and Thresholding
Benedikt M. Potscher, Hannes Leeb
math.STstat.MEstat.MLarXiv:0711.0660v22007
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach
Hao-Hsuan Chang, Hao Song, Yang Yi +3
cs.LGstat.MLarXiv:1810.11758v12018
Uniform Race: Parameter-Free Approximate Rejection Sampling
Seiyun Shin, Juhyeong Pang, Kwang-Sung Jun
cs.LGstat.MLarXiv:2609.34639v12026
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit +2
cs.LGcs.IRcs.SEarXiv:1909.09436v32019
A la Carte - Learning Fast Kernels
Zichao Yang, Alexander J. Smola, Le Song +1
cs.LGstat.MLarXiv:1412.6493v12014
Distral: Robust Multitask Reinforcement Learning
Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki +5
cs.LGstat.MLarXiv:1707.04175v12017
Deep learning with Elastic Averaging SGD
Sixin Zhang, Anna Choromanska, Yann LeCun
cs.LGstat.MLarXiv:1412.6651v82014
Tolerance-Based Fairness Auditing: Violation Certification and Sensitivity Screening
Jie Tang, Chuanlong Xie, Lixing Zhu
stat.MLcs.LGstat.MEarXiv:2610.01005v12026
Exactly Computing the Local Lipschitz Constant of ReLU Networks
Matt Jordan, Alexandros G. Dimakis
stat.MLcs.LGarXiv:2003.01219v22020
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
Joana Lorenz, Maria Inês Silva, David Aparício +2
cs.LGstat.MLarXiv:2005.14635v22020
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
stat.MLcs.LGarXiv:1810.11646v12018
PAC-Bayesian Inequalities for Martingales
Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi +2
cs.LGcs.ITmath.ITarXiv:1110.6886v32011
Quantifying Differences in Reward Functions
Adam Gleave, Michael Dennis, Shane Legg +2
cs.LGcs.AIstat.MLarXiv:2006.13900v32020
On the Complexity of Best Arm Identification in Multi-Armed Bandit Models
Emilie Kaufmann, Olivier Cappé, Aurélien Garivier
stat.MLcs.LGarXiv:1407.4443v22014
k-NN Regression Adapts to Local Intrinsic Dimension
Samory Kpotufe
stat.MLarXiv:1110.4300v12011
AudioPaLM: A Large Language Model That Can Speak and Listen
Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen +27
cs.CLcs.AIcs.SDarXiv:2306.12925v12023
Deep Hyperspherical Learning
Weiyang Liu, Yan-Ming Zhang, Xingguo Li +4
cs.LGcs.CVstat.MLarXiv:1711.03189v52017
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
Dongjun Kim, Seungjae Shin, Kyungwoo Song +2
cs.LGcs.AIstat.MLarXiv:2106.05527v52021
Deep learning for time series classification: a review
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber +2
cs.LGcs.AIstat.MLarXiv:1809.04356v42018
The NetHack Learning Environment
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller +4
cs.LGcs.AIcs.CLarXiv:2006.13760v22020
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Yasar Sinan Nasir, Dongning Guo
eess.SPcs.ITstat.MLarXiv:1808.00490v32018
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Huan Zhang, Shiqi Wang, Kaidi Xu +5
cs.LGcs.CRcs.CVarXiv:2208.05740v22022
CatBoost: gradient boosting with categorical features support
Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
cs.LGcs.MSstat.MLarXiv:1810.11363v12018
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
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava +2
cs.LGstat.MLarXiv:1907.04931v42019
Nonparametric semi-supervised learning of class proportions
Shantanu Jain, Martha White, Michael W. Trosset +1
stat.MLcs.LGarXiv:1601.01944v12016
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
David Salinas, Valentin Flunkert, Jan Gasthaus
cs.AIcs.LGstat.MLarXiv:1704.04110v32017
Adaptive Evolutionary Clustering
Kevin S. Xu, Mark Kliger, Alfred O. Hero III
cs.LGstat.MLarXiv:1104.1990v32011
Image Synthesis From Reconfigurable Layout and Style
Wei Sun, Tianfu Wu
cs.CVstat.MLarXiv:1908.07500v12019
Model Selection Techniques -- An Overview
Jie Ding, Vahid Tarokh, Yuhong Yang
stat.MLcs.ITcs.LGarXiv:1810.09583v12018
k-hop Graph Neural Networks
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis
stat.MLcs.LGarXiv:1907.06051v22019
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Ron Amit, Ron Meir
stat.MLcs.AIcs.LGarXiv:1711.01244v82017

Machine Learning (stat)

Momentum in large-batch training: Polyak enlarges the critical batch size, Nesterov improves data efficiency

Is Homophily a Necessity for Graph Neural Networks?

Compositionality decomposed: how do neural networks generalise?

A Neural Autoregressive Approach to Collaborative Filtering

Quickest Change Detection with Diffusion-Integrated Scores

A Distributional Perspective on Reinforcement Learning

Dense Structural Compression of Transformers via Gauge-Correct Channel Removal

Iterative Machine Teaching

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

Kronecker Graphs: An Approach to Modeling Networks

The Expressive Power of Low-Rank Adaptation

Optimal random quantisers for spherically symmetric distributions

Understanding and Robustifying Differentiable Architecture Search

Low Data Drug Discovery with One-shot Learning

Hypothesis Testing for Automated Community Detection in Networks

Nonparametric ridge estimation

Finite-Sample Analysis of Proximal Gradient TD Algorithms

A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network

Adversarial Objects Against LiDAR-Based Autonomous Driving Systems

An Accurate and Single-Communication Federated Inference Algorithm

Fast Two-Sample Testing with Analytic Representations of Probability Measures

Sharp Bounds on the Approximation Rates, Metric Entropy, and $n$-widths of Shallow Neural Networks

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

DyNet: The Dynamic Neural Network Toolkit

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

Random Vector Functional Link Networks for Function Approximation on Manifolds

On the Distribution of Penalized Maximum Likelihood Estimators: The LASSO, SCAD, and Thresholding

Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach

Uniform Race: Parameter-Free Approximate Rejection Sampling

CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

A la Carte - Learning Fast Kernels

Distral: Robust Multitask Reinforcement Learning

Deep learning with Elastic Averaging SGD

Tolerance-Based Fairness Auditing: Violation Certification and Sensitivity Screening

Exactly Computing the Local Lipschitz Constant of ReLU Networks

Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity

Removing Hidden Confounding by Experimental Grounding

PAC-Bayesian Inequalities for Martingales

Quantifying Differences in Reward Functions

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

k-NN Regression Adapts to Local Intrinsic Dimension

AudioPaLM: A Large Language Model That Can Speak and Listen

Deep Hyperspherical Learning

Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation

Deep learning for time series classification: a review

The NetHack Learning Environment

Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

General Cutting Planes for Bound-Propagation-Based Neural Network Verification

CatBoost: gradient boosting with categorical features support

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

GraphSAINT: Graph Sampling Based Inductive Learning Method

Nonparametric semi-supervised learning of class proportions

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

Adaptive Evolutionary Clustering

Image Synthesis From Reconfigurable Layout and Style

Model Selection Techniques -- An Overview

k-hop Graph Neural Networks

Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory