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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7,801 to 7,860 of 9,704

A Survey on Multi-View Clustering
Guoqing Chao, Shiliang Sun, Jinbo Bi
cs.LGstat.MLarXiv:1712.06246v22017
Is Data Clustering in Adversarial Settings Secure?
Battista Biggio, Ignazio Pillai, Samuel Rota Bulò +3
cs.LGcs.CRcs.CVarXiv:1811.09982v12018
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data
Guansong Pang, Anton van den Hengel, Chunhua Shen +1
cs.LGcs.AIcs.CRarXiv:2009.06847v22020
Classification vs regression in overparameterized regimes: Does the loss function matter?
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian +3
cs.LGcs.ITstat.MLarXiv:2005.08054v22020
Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions
Mengdi Wang, Ethan X. Fang, Han Liu
stat.MLarXiv:1411.3803v12014
Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization
Jingyuan Xia, Shengxi Li, Jun-Jie Huang +2
cs.LGmath.OCstat.MLarXiv:2009.04899v72020
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca +3
cs.LGstat.MLarXiv:2006.10637v32020
Imbalanced Continual Learning with Partitioning Reservoir Sampling
Chris Dongjoo Kim, Jinseo Jeong, Gunhee Kim
cs.LGcs.AIcs.CVarXiv:2009.03632v12020
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
Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
cs.LGstat.MLarXiv:2009.02463v12020
Deep Learning Approximation for Stochastic Control Problems
Jiequn Han, Weinan E
cs.LGcs.AIcs.NEarXiv:1611.07422v12016
Principled Reinforcement Learning with Human Feedback from Pairwise or $K$-wise Comparisons
Banghua Zhu, Jiantao Jiao, Michael I. Jordan
cs.LGcs.AIcs.HCarXiv:2301.11270v52023
Confident Learning: Estimating Uncertainty in Dataset Labels
Curtis G. Northcutt, Lu Jiang, Isaac L. Chuang
stat.MLcs.LGarXiv:1911.00068v62019
Model-Based Domain Generalization
Alexander Robey, George J. Pappas, Hamed Hassani
stat.MLcs.AIcs.LGarXiv:2102.11436v52021
A Deep Learning Approach to Structured Signal Recovery
Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk
cs.LGstat.MLarXiv:1508.04065v12015
Subgraph Neural Networks
Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li +1
cs.LGcs.SIstat.MLarXiv:2006.10538v32020
Fairness in Criminal Justice Risk Assessments: The State of the Art
Richard A. Berk, Hoda Heidari, Shahin Jabbari +2
stat.MLarXiv:1703.09207v22017
Muon meets Tamed Langevin: Momentum Preconditioning beyond Convex and gradient-Lipschitz Potentials
Nikolaos Makras, Sotirios Sabanis
cs.LGmath.OCmath.PRarXiv:2610.02158v12026
Defending Neural Backdoors via Generative Distribution Modeling
Ximing Qiao, Yukun Yang, Hai Li
cs.LGstat.MLarXiv:1910.04749v22019
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
John Miller, Rohan Taori, Aditi Raghunathan +6
cs.LGstat.MLarXiv:2107.04649v22021
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems
Christopher De Sa, Kunle Olukotun, Christopher Ré
cs.LGmath.OCstat.MLarXiv:1411.1134v32014
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Christophe Muller, Ayub Kharel, Alex Luedtke +6
stat.MLcs.LGarXiv:2610.08078v12026
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer +1
cs.LGstat.MLarXiv:2006.10503v32020
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
Furui Cheng, Yao Ming, Huamin Qu
cs.LGcs.HCstat.MLarXiv:2008.08353v12020
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
Md Abul Bashar, Richi Nayak
cs.CVcs.LGstat.MLarXiv:2008.09567v22020
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
Karan Goel, Albert Gu, Yixuan Li +1
cs.LGcs.AIcs.CVarXiv:2008.06775v12020
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li, Chenjie Gu, Thomas Dullien +2
cs.LGstat.MLarXiv:1904.12787v22019
Robust Validation: Confident Predictions Even When Distributions Shift
Maxime Cauchois, Suyash Gupta, Alnur Ali +1
stat.MLcs.LGstat.MEarXiv:2008.04267v32020
Optimal Sequential Annotations for Off-Policy Evaluation
Woojin Chae, Ezinne Nwankwo, Haitong Qin +1
stat.MEcs.LGstat.MLarXiv:2609.26707v12026
Censoring Representations with an Adversary
Harrison Edwards, Amos Storkey
cs.LGcs.AIstat.MLarXiv:1511.05897v32015
Domain Adaptive Neural Networks for Object Recognition
Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang
cs.CVcs.AIcs.LGarXiv:1409.6041v12014
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
cs.LGstat.MLarXiv:1906.01227v22019
On the Convergence of FedAvg on Non-IID Data
Xiang Li, Kaixuan Huang, Wenhao Yang +2
stat.MLcs.LGmath.OCarXiv:1907.02189v42019
Replay in the Silent Degrees of Freedom: Continual Learning Without an Offline Phase
Yanhai Zhang, Jie Zhang, Chi Xu
cs.LGcs.NEstat.MLarXiv:2609.31630v22026
On the Practical Computational Power of Finite Precision RNNs for Language Recognition
Gail Weiss, Yoav Goldberg, Eran Yahav
cs.LGcs.CLstat.MLarXiv:1805.04908v12018
Interpretability via Model Extraction
Osbert Bastani, Carolyn Kim, Hamsa Bastani
cs.LGcs.CYstat.MLarXiv:1706.09773v42017
Uniform Sampling for Matrix Approximation
Michael B. Cohen, Yin Tat Lee, Cameron Musco +3
cs.DScs.LGstat.MLarXiv:1408.5099v12014
Learning Deep Networks from Noisy Labels with Dropout Regularization
Ishan Jindal, Matthew Nokleby, Xuewen Chen
cs.CVcs.LGstat.MLarXiv:1705.03419v12017
A Plea for Neutral Comparison Studies in Computational Sciences
Anne-Laure Boulesteix, Manuel J. A. Eugster
stat.COcs.CVstat.MEarXiv:1208.2651v12012
Supervised learning with quantum enhanced feature spaces
Vojtech Havlicek, Antonio D. Córcoles, Kristan Temme +4
quant-phstat.MLarXiv:1804.11326v22018
DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
Xiangfei Qiu, Xingjian Wu, Yan Lin +3
cs.LGstat.MLarXiv:2412.10859v32024
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
Po-Yu Chen, Ivan W. Selesnick
cs.CVcs.LGstat.MLarXiv:1308.5038v22013
Scalable Kernel Methods via Doubly Stochastic Gradients
Bo Dai, Bo Xie, Niao He +4
cs.LGstat.MLarXiv:1407.5599v42014
Scalable Differentiable Physics for Learning and Control
Yi-Ling Qiao, Junbang Liang, Vladlen Koltun +1
cs.LGcs.GRstat.MLarXiv:2007.02168v12020
Causal Discovery in Physical Systems from Videos
Yunzhu Li, Antonio Torralba, Animashree Anandkumar +2
cs.LGcs.CVstat.MLarXiv:2007.00631v32020
Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski, Stephan Günnemann
cs.LGcs.CRcs.SIarXiv:1809.01093v32018
On Constrained Spectral Clustering and Its Applications
Xiang Wang, Buyue Qian, Ian Davidson
cs.LGstat.MLarXiv:1201.5338v22012
LSQ+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat, Jinwon Lee, Markus Nagel +2
cs.CVcs.LGstat.MLarXiv:2004.09576v12020
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Oron Anschel, Nir Baram, Nahum Shimkin
cs.AIcs.LGstat.MLarXiv:1611.01929v42016
VeLO: Training Versatile Learned Optimizers by Scaling Up
Luke Metz, James Harrison, C. Daniel Freeman +8
cs.LGmath.OCstat.MLarXiv:2211.09760v12022
A review of learning vector quantization classifiers
David Nova, Pablo A. Estevez
cs.LGastro-ph.IMcs.NEarXiv:1509.07093v12015
Towards Understanding Regularization in Batch Normalization
Ping Luo, Xinjiang Wang, Wenqi Shao +1
cs.LGcs.CVeess.SYarXiv:1809.00846v42018
Multiple Source Adaptation and the Renyi Divergence
Yishay Mansour, Mehryar Mohri, Afshin Rostamizadeh
cs.LGstat.MLarXiv:1205.2628v12012
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
Jason Jo, Yoshua Bengio
cs.LGstat.MLarXiv:1711.11561v12017
Projected Subgradient Methods for Learning Sparse Gaussians
John Duchi, Stephen Gould, Daphne Koller
cs.LGstat.MLarXiv:1206.3249v12012
Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning
Shaddin Dughmi, Alireza F. Pour
cs.LGstat.MLarXiv:2609.10886v12026
Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects
Tianxi Li, Jie Ding
stat.MLcs.LGstat.AParXiv:2610.07755v12026
Lipschitz Recurrent Neural Networks
N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga +2
cs.LGmath.DSstat.MLarXiv:2006.12070v32020
Matérn Gaussian processes on Riemannian manifolds
Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky +1
stat.MLcs.LGarXiv:2006.10160v62020
Deepr: A Convolutional Net for Medical Records
Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe +1
stat.MLcs.LGarXiv:1607.07519v12016

Machine Learning (stat)

A Survey on Multi-View Clustering

Is Data Clustering in Adversarial Settings Secure?

Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

Classification vs regression in overparameterized regimes: Does the loss function matter?

Stochastic Compositional Gradient Descent: Algorithms for Minimizing Compositions of Expected-Value Functions

Meta-learning based Alternating Minimization Algorithm for Non-convex Optimization

Temporal Graph Networks for Deep Learning on Dynamic Graphs

Imbalanced Continual Learning with Partitioning Reservoir Sampling

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

Unifying Clustered and Non-stationary Bandits

Deep Learning Approximation for Stochastic Control Problems

Principled Reinforcement Learning with Human Feedback from Pairwise or $K$-wise Comparisons

Confident Learning: Estimating Uncertainty in Dataset Labels

Model-Based Domain Generalization

A Deep Learning Approach to Structured Signal Recovery

Subgraph Neural Networks

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

Muon meets Tamed Langevin: Momentum Preconditioning beyond Convex and gradient-Lipschitz Potentials

Defending Neural Backdoors via Generative Distribution Modeling

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems

ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models

TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks

Model Patching: Closing the Subgroup Performance Gap with Data Augmentation

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

Robust Validation: Confident Predictions Even When Distributions Shift

Optimal Sequential Annotations for Off-Policy Evaluation

Censoring Representations with an Adversary

Domain Adaptive Neural Networks for Object Recognition

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

On the Convergence of FedAvg on Non-IID Data

Replay in the Silent Degrees of Freedom: Continual Learning Without an Offline Phase

On the Practical Computational Power of Finite Precision RNNs for Language Recognition

Interpretability via Model Extraction

Uniform Sampling for Matrix Approximation

Learning Deep Networks from Noisy Labels with Dropout Regularization

A Plea for Neutral Comparison Studies in Computational Sciences

Supervised learning with quantum enhanced feature spaces

DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting

Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization

Scalable Kernel Methods via Doubly Stochastic Gradients

Scalable Differentiable Physics for Learning and Control

Causal Discovery in Physical Systems from Videos

Adversarial Attacks on Node Embeddings via Graph Poisoning

On Constrained Spectral Clustering and Its Applications

LSQ+: Improving low-bit quantization through learnable offsets and better initialization

Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning

VeLO: Training Versatile Learned Optimizers by Scaling Up

A review of learning vector quantization classifiers

Towards Understanding Regularization in Batch Normalization

Multiple Source Adaptation and the Renyi Divergence

Measuring the tendency of CNNs to Learn Surface Statistical Regularities

Projected Subgradient Methods for Learning Sparse Gaussians

Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning

Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects

Lipschitz Recurrent Neural Networks

Matérn Gaussian processes on Riemannian manifolds

Deepr: A Convolutional Net for Medical Records