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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8,041 to 8,100 of 9,699

Linear Exponential Quadratic Gaussian Covariance Steering
Chiran B. Cherian, Yasemin Isik, Abhishek Halder
math.OCcs.AIcs.LGarXiv:2609.12463v12026
Strategies for Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes +4
cs.LGstat.MLarXiv:1905.12265v32019
Reinforcement Learning for Relation Classification from Noisy Data
Jun Feng, Minlie Huang, Li Zhao +2
cs.IRcs.LGstat.MLarXiv:1808.08013v12018
A Survey on Practical Applications of Multi-Armed and Contextual Bandits
Djallel Bouneffouf, Irina Rish
cs.LGstat.MLarXiv:1904.10040v12019
Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers
Aaron Griffith, Andrew Pomerance, Daniel J. Gauthier
cs.LGnlin.CDstat.MLarXiv:1910.00659v22019
Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions
Haijie Xu, Chen Zhang
stat.MLcs.LGarXiv:2608.28991v12026
Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning
Alireza Abdollahpoorrostam, Ehsan Sharifian, Buse Şen +2
cs.LGmath.OCstat.MLarXiv:2609.31363v12026
Transfer Learning under High-dimensional Generalized Linear Models
Ye Tian, Yang Feng
stat.MLcs.LGstat.MEarXiv:2105.14328v42021
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
Yuchen Zhang, John C. Duchi, Martin J. Wainwright
math.STcs.LGstat.MLarXiv:1305.5029v22013
Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)
John Gideon, Melvin G McInnis, Emily Mower Provost
cs.LGcs.SDeess.ASarXiv:1903.12094v22019
Measuring abstract reasoning in neural networks
David G. T. Barrett, Felix Hill, Adam Santoro +2
cs.LGstat.MLarXiv:1807.04225v12018
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
Po-Ling Loh, Martin J. Wainwright
math.STcs.ITstat.MLarXiv:1305.2436v22013
Elements of Sequential Monte Carlo
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
stat.MLcs.LGstat.COarXiv:1903.04797v32019
Nash Learning from Human Feedback
Rémi Munos, Michal Valko, Daniele Calandriello +14
stat.MLcs.AIcs.GTarXiv:2312.00886v42023
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Tian Guo, Tao Lin, Nino Antulov-Fantulin
cs.LGstat.MLarXiv:1905.12034v12019
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
Filipe de Avila Belbute-Peres, Thomas D. Economon, J. Zico Kolter
cs.LGphysics.comp-phstat.MLarXiv:2007.04439v32020
Neural Fine-Gray: Monotonic neural networks for competing risks
Vincent Jeanselme, Chang Ho Yoon, Brian Tom +1
cs.LGcs.AIstat.MLarXiv:2305.06703v12023
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Yuzhe Yang, Zhi Xu
cs.LGcs.CVstat.MLarXiv:2006.07529v22020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
cs.LGstat.MLarXiv:2006.09239v22020
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Yifan Wu, Ezra Winston, Divyansh Kaushik +1
cs.LGstat.MLarXiv:1903.01689v22019
Gotta Go Fast When Generating Data with Score-Based Models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer +2
cs.LGcs.CVmath.OCarXiv:2105.14080v12021
Counterfactual Risk Assessments, Evaluation, and Fairness
Amanda Coston, Alan Mishler, Edward H. Kennedy +1
stat.MLcs.CYcs.LGarXiv:1909.00066v32019
Learning Good State and Action Representations via Tensor Decomposition
Chengzhuo Ni, Yaqi Duan, Munther Dahleh +2
stat.MLcs.LGarXiv:2105.01136v22021
Convergence and Dynamical Behavior of the ADAM Algorithm for Non-Convex Stochastic Optimization
Anas Barakat, Pascal Bianchi
stat.MLcs.LGmath.CAarXiv:1810.02263v42018
Smart Augmentation - Learning an Optimal Data Augmentation Strategy
Joseph Lemley, Shabab Bazrafkan, Peter Corcoran
cs.AIcs.LGstat.MLarXiv:1703.08383v12017
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks
Like Hui, Mikhail Belkin
cs.LGstat.MLarXiv:2006.07322v52020
Membership Inference via Pairwise Likelihood Ratios
Shengjie Niu, Zebin Yun, Yeheng Ge +1
stat.MLcs.LGarXiv:2609.12367v12026
Sorting out Lipschitz function approximation
Cem Anil, James Lucas, Roger Grosse
cs.LGstat.MLarXiv:1811.05381v22018
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan, Quoc V. Le
cs.LGcs.CVstat.MLarXiv:1905.11946v52019
Clustering Partially Observed Graphs via Convex Optimization
Yudong Chen, Ali Jalali, Sujay Sanghavi +1
cs.LGstat.MLarXiv:1104.4803v42011
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
Wei-Ning Hsu, Yu Zhang, James Glass
cs.LGcs.CLcs.SDarXiv:1709.07902v12017
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Jiayu Yao, Weiwei Pan, Soumya Ghosh +1
cs.LGstat.MLarXiv:1906.09686v12019
Acceleration via Symplectic Discretization of High-Resolution Differential Equations
Bin Shi, Simon S. Du, Weijie J. Su +1
math.OCcs.LGcs.NAarXiv:1902.03694v22019
A Unified Lottery Ticket Hypothesis for Graph Neural Networks
Tianlong Chen, Yongduo Sui, Xuxi Chen +2
cs.LGcs.AIstat.MLarXiv:2102.06790v22021
A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm
Maher Al-Shoukairi, Philip Schniter, Bhaskar D. Rao
cs.LGstat.MLarXiv:1703.03044v22017
Surrogate gradients for analog neuromorphic computing
Benjamin Cramer, Sebastian Billaudelle, Simeon Kanya +9
cs.NEcs.ETcs.LGarXiv:2006.07239v32020
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu +1
cs.LGstat.MLarXiv:1910.00189v22019
Natural Analysts in Adaptive Data Analysis
Tijana Zrnic, Moritz Hardt
cs.LGmath.STstat.MLarXiv:1901.11143v22019
Decentralized Collaborative Learning of Personalized Models over Networks
Paul Vanhaesebrouck, Aurélien Bellet, Marc Tommasi
cs.LGcs.AIcs.DCarXiv:1610.05202v22016
Mapping the stereotyped behaviour of freely-moving fruit flies
Gordon J. Berman, Daniel M. Choi, William Bialek +1
q-bio.QMcs.CVphysics.bio-pharXiv:1310.4249v22013
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Aaron Defazio, Francis Bach, Simon Lacoste-Julien
cs.LGmath.OCstat.MLarXiv:1407.0202v32014
An optimal control perspective on diffusion-based generative modeling
Julius Berner, Lorenz Richter, Karen Ullrich
cs.LGmath.OCstat.MLarXiv:2211.01364v32022
Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks
Ahmad Halimi Razlighi, Mohammad Siddiqur Rahman, Maximilian H. V. Tillmann +2
eess.SPcs.LGeess.IVarXiv:2609.03977v12026
Predicting wind pressures around circular cylinders using machine learning techniques
Gang Hu, K. C. S. Kwok
cs.LGphysics.flu-dynstat.MLarXiv:1901.06752v12019
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Matthias Feurer, Katharina Eggensperger, Stefan Falkner +2
cs.LGstat.MLarXiv:2007.04074v32020
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Aditya Grover, Jiaming Song, Alekh Agarwal +4
stat.MLcs.LGcs.NEarXiv:1906.09531v22019
Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
Jianyu Wang, Gauri Joshi
cs.LGcs.DCstat.MLarXiv:1808.07576v32018
Explaining Neural Scaling Laws
Yasaman Bahri, Ethan Dyer, Jared Kaplan +2
cs.LGcond-mat.dis-nnstat.MLarXiv:2102.06701v22021
PDE-constrained inverse problems at the $\sqrt{n}$ rate via debiased physics-informed neural networks
Yves Atchade, Debarghya Mukherjee
math.STstat.MEstat.MLarXiv:2609.12301v12026
Multisource and Multitemporal Data Fusion in Remote Sensing
Pedram Ghamisi, Behnood Rasti, Naoto Yokoya +9
cs.LGeess.SPstat.MLarXiv:1812.08287v12018
Handling Incomplete Heterogeneous Data using VAEs
Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani +1
cs.LGcs.AIstat.MLarXiv:1807.03653v42018
Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis
Brendon Lutnick, Brandon Ginley, Darshana Govind +7
eess.IVcs.CVcs.HCarXiv:1812.07509v12018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner, Geoff Pleiss, David Bindel +2
cs.LGstat.MLarXiv:1809.11165v62018
Explainable Machine Learning in Deployment
Umang Bhatt, Alice Xiang, Shubham Sharma +7
cs.LGcs.AIcs.CYarXiv:1909.06342v42019
SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth
stat.MLcs.LGarXiv:2502.02472v32025
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles, Matthijs Douze, Yann Ollivier +2
stat.MLcs.CRcs.LGarXiv:1908.11229v12019
On the Importance of Strong Baselines in Bayesian Deep Learning
Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal
cs.LGstat.MLarXiv:1811.09385v22018
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Geoffrey Roeder, Yuhuai Wu, David Duvenaud
stat.MLcs.LGarXiv:1703.09194v32017
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification
Edgar Dobriban, Stefan Wager
math.STstat.MLarXiv:1507.03003v22015
A Variational Dirichlet Framework for Out-of-Distribution Detection
Wenhu Chen, Yilin Shen, Hongxia Jin +1
cs.LGcs.AIcs.CVarXiv:1811.07308v42018

Machine Learning (stat)

Linear Exponential Quadratic Gaussian Covariance Steering

Strategies for Pre-training Graph Neural Networks

Reinforcement Learning for Relation Classification from Noisy Data

A Survey on Practical Applications of Multi-Armed and Contextual Bandits

Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers

Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions

Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning

Transfer Learning under High-dimensional Generalized Linear Models

Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates

Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)

Measuring abstract reasoning in neural networks

Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima

Elements of Sequential Monte Carlo

Nash Learning from Human Feedback

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction

Neural Fine-Gray: Monotonic neural networks for competing risks

Rethinking the Value of Labels for Improving Class-Imbalanced Learning

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

Gotta Go Fast When Generating Data with Score-Based Models

Counterfactual Risk Assessments, Evaluation, and Fairness

Learning Good State and Action Representations via Tensor Decomposition

Convergence and Dynamical Behavior of the ADAM Algorithm for Non-Convex Stochastic Optimization

Smart Augmentation - Learning an Optimal Data Augmentation Strategy

Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks

Membership Inference via Pairwise Likelihood Ratios

Sorting out Lipschitz function approximation

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Clustering Partially Observed Graphs via Convex Optimization

Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

Quality of Uncertainty Quantification for Bayesian Neural Network Inference

Acceleration via Symplectic Discretization of High-Resolution Differential Equations

A Unified Lottery Ticket Hypothesis for Graph Neural Networks

A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm

Surrogate gradients for analog neuromorphic computing

The Non-IID Data Quagmire of Decentralized Machine Learning

Natural Analysts in Adaptive Data Analysis

Decentralized Collaborative Learning of Personalized Models over Networks

Mapping the stereotyped behaviour of freely-moving fruit flies

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

An optimal control perspective on diffusion-based generative modeling

Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks

Predicting wind pressures around circular cylinders using machine learning techniques

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms

Explaining Neural Scaling Laws

PDE-constrained inverse problems at the $\sqrt{n}$ rate via debiased physics-informed neural networks

Multisource and Multitemporal Data Fusion in Remote Sensing

Handling Incomplete Heterogeneous Data using VAEs

Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

Explainable Machine Learning in Deployment

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations

White-box vs Black-box: Bayes Optimal Strategies for Membership Inference

On the Importance of Strong Baselines in Bayesian Deep Learning

Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification

A Variational Dirichlet Framework for Out-of-Distribution Detection