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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1,561 to 1,620 of 6,782

Variational Federated Multi-Task Learning
Luca Corinzia, Ami Beuret, Joachim M. Buhmann
cs.LGstat.MLarXiv:1906.06268v22019
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts
Lucy Harris, Andrew T. T. McRae, Matthew Chantry +2
physics.ao-phcs.AIcs.CVarXiv:2204.02028v22022
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
Johannes Kirschner, Mojmír Mutný, Nicole Hiller +2
cs.LGstat.MLarXiv:1902.03229v22019
Robust Ensemble Clustering Using Probability Trajectories
Dong Huang, Jian-Huang Lai, Chang-Dong Wang
stat.MLcs.LGarXiv:1606.01160v12016
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses
Po-Ling Loh, Martin J. Wainwright
stat.MLmath.STarXiv:1212.0478v22012
Riemannian Continuous Normalizing Flows
Emile Mathieu, Maximilian Nickel
stat.MLcs.LGarXiv:2006.10605v22020
Pushing the limits of unconstrained machine-learned interatomic potentials
Filippo Bigi, Paolo Pegolo, Arslan Mazitov +2
physics.chem-phstat.MLarXiv:2601.16195v32026
Test-time regression: a unifying framework for designing sequence models with associative memory
Ke Alexander Wang, Jiaxin Shi, Emily B. Fox
cs.LGcs.AIcs.NEarXiv:2501.12352v32025
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman +2
cs.LGstat.MLarXiv:1912.07768v12019
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hoi-To Wai, Zhuoran Yang, Zhaoran Wang +1
cs.LGmath.OCstat.MLarXiv:1806.00877v42018
Natural Compression for Distributed Deep Learning
Samuel Horvath, Chen-Yu Ho, Ludovit Horvath +3
cs.LGmath.OCstat.MLarXiv:1905.10988v32019
Unsupervised Learning by Competing Hidden Units
Dmitry Krotov, John Hopfield
cs.LGcs.CVcs.NEarXiv:1806.10181v22018
Contrastive Code Representation Learning
Paras Jain, Ajay Jain, Tianjun Zhang +3
cs.LGcs.AIcs.PLarXiv:2007.04973v42020
Optimal computational and statistical rates of convergence for sparse nonconvex learning problems
Zhaoran Wang, Han Liu, Tong Zhang
stat.MLarXiv:1306.4960v52013
The HSIC Bottleneck: Deep Learning without Back-Propagation
Wan-Duo Kurt Ma, J. P. Lewis, W. Bastiaan Kleijn
cs.LGstat.MLarXiv:1908.01580v32019
Douglas-Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems
Guoyin Li, Ting Kei Pong
math.OCstat.MLarXiv:1409.8444v52014
Robustness of Graph Neural Networks at Scale
Simon Geisler, Tobias Schmidt, Hakan Şirin +3
cs.LGstat.MLarXiv:2110.14038v42021
The Inductive Bias of Quantum Kernels
Jonas M. Kübler, Simon Buchholz, Bernhard Schölkopf
quant-phstat.MLarXiv:2106.03747v22021
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching
Runzhong Wang, Junchi Yan, Xiaokang Yang
cs.LGcs.CVstat.MLarXiv:1911.11308v32019
A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization
Kejun Huang, Nicholas D. Sidiropoulos, Athanasios P. Liavas
stat.MLcs.LGmath.OCarXiv:1506.04209v22015
Deep Multimodal Subspace Clustering Networks
Mahdi Abavisani, Vishal M. Patel
cs.LGcs.AIcs.CVarXiv:1804.06498v32018
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Tian Guo, Tao Lin, Nino Antulov-Fantulin
cs.LGstat.MLarXiv:1905.12034v12019
The group fused Lasso for multiple change-point detection
Kevin Bleakley, Jean-Philippe Vert
q-bio.QMstat.MLarXiv:1106.4199v12011
The Emergence of Spectral Universality in Deep Networks
Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli
stat.MLcs.LGarXiv:1802.09979v12018
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Víctor Campos, Alexander Trott, Caiming Xiong +3
cs.LGcs.AIstat.MLarXiv:2002.03647v42020
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
Yuma Koizumi, Shoichiro Saito, Hisashi Uematsum Yuta Kawachi +1
stat.MLcs.LGcs.SDarXiv:1810.09133v12018
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Sujay Khandagale, Han Xiao, Rohit Babbar
cs.LGstat.MLarXiv:1904.08249v22019
Graphical-model based estimation and inference for differential privacy
Ryan McKenna, Daniel Sheldon, Gerome Miklau
cs.LGcs.CRstat.MLarXiv:1901.09136v12019
Provably Consistent Partial-Label Learning
Lei Feng, Jiaqi Lv, Bo Han +5
cs.LGstat.MLarXiv:2007.08929v22020
Practical Efficiency of Muon for Pretraining
Essential AI, :, Ishaan Shah +22
cs.LGstat.MLarXiv:2505.02222v42025
Emotion in Reinforcement Learning Agents and Robots: A Survey
Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
cs.LGcs.AIcs.HCarXiv:1705.05172v12017
Fairwashing: the risk of rationalization
Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau +3
cs.LGstat.MLarXiv:1901.09749v32019
Insights into LSTM Fully Convolutional Networks for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi
cs.LGstat.MLarXiv:1902.10756v32019
Modeling Information Propagation with Survival Theory
Manuel Gomez Rodriguez, Jure Leskovec, Bernhard Schoelkopf
cs.SIcs.DSphysics.soc-pharXiv:1305.3616v12013
Distributed Online Convex Optimization with Time-Varying Coupled Inequality Constraints
Xinlei Yi, Xiuxian Li, Lihua Xie +1
math.OCcs.DCcs.LGarXiv:1903.04277v22019
An Evaluation of the Human-Interpretability of Explanation
Isaac Lage, Emily Chen, Jeffrey He +4
cs.LGstat.MLarXiv:1902.00006v22019
TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning
Seongjin Choi, Jiwon Kim, Hwasoo Yeo
cs.LGstat.MLarXiv:2007.14189v42020
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri +2
cs.LGcs.DCmath.OCarXiv:2003.10422v32020
The Synthesizability of Molecules Proposed by Generative Models
Wenhao Gao, Connor W. Coley
q-bio.QMcs.LGstat.MLarXiv:2002.07007v12020
Federated Learning of a Mixture of Global and Local Models
Filip Hanzely, Peter Richtárik
cs.LGcs.DCmath.OCarXiv:2002.05516v32020
Multi-source Distilling Domain Adaptation
Sicheng Zhao, Guangzhi Wang, Shanghang Zhang +7
cs.LGcs.CVstat.MLarXiv:1911.11554v22019
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk +4
cs.LGcs.CVstat.MLarXiv:1911.09785v22019
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti +1
stat.MLcs.LGarXiv:1907.04809v42019
Improving Variational Auto-Encoders using Householder Flow
Jakub M. Tomczak, Max Welling
cs.LGstat.MLarXiv:1611.09630v42016
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
stat.MLcs.LGarXiv:1612.01474v32016
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Itay Safran, Ohad Shamir
cs.LGcs.NEstat.MLarXiv:1610.09887v32016
A Fusion Approach for Efficient Human Skin Detection
Wei Ren Tan, Chee Seng Chan, Pratheepan Yogarajah +1
cs.CVstat.MLarXiv:1410.3751v12014
Graph Neural Ordinary Differential Equations
Michael Poli, Stefano Massaroli, Junyoung Park +3
cs.LGcs.AIstat.MLarXiv:1911.07532v42019
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
Vincent Le Guen, Nicolas Thome
stat.MLcs.LGarXiv:1909.09020v42019
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
Rong Ge, Sham M. Kakade, Rahul Kidambi +1
cs.LGmath.OCstat.MLarXiv:1904.12838v22019
Randomized Sketches of Convex Programs with Sharp Guarantees
Mert Pilanci, Martin J. Wainwright
cs.ITcs.DSmath.OCarXiv:1404.7203v12014
A Retrieve-and-Edit Framework for Predicting Structured Outputs
Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren +1
stat.MLcs.LGarXiv:1812.01194v12018
Multilingual Topic Models for Unaligned Text
Jordan Boyd-Graber, David Blei
cs.CLcs.IRcs.LGarXiv:1205.2657v12012
The Multiscale Laplacian Graph Kernel
Risi Kondor, Horace Pan
stat.MLarXiv:1603.06186v22016
Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière +4
stat.MLcs.LGarXiv:2002.02428v22020
Randomized sketches for kernels: Fast and optimal non-parametric regression
Yun Yang, Mert Pilanci, Martin J. Wainwright
stat.MLcs.DScs.LGarXiv:1501.06195v12015
How do infinite width bounded norm networks look in function space?
Pedro Savarese, Itay Evron, Daniel Soudry +1
cs.LGstat.MLarXiv:1902.05040v12019
Avoiding Latent Variable Collapse With Generative Skip Models
Adji B. Dieng, Yoon Kim, Alexander M. Rush +1
stat.MLcs.CLcs.LGarXiv:1807.04863v22018
Learning Controllable Fair Representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover +2
cs.LGcs.AIstat.MLarXiv:1812.04218v32018
C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework
Pablo Sprechmann, Ignacio Ramírez, Guillermo Sapiro +1
stat.MLcs.CVarXiv:1006.1346v22010

Machine Learning (stat)

Variational Federated Multi-Task Learning

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces

Robust Ensemble Clustering Using Probability Trajectories

Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses

Riemannian Continuous Normalizing Flows

Pushing the limits of unconstrained machine-learned interatomic potentials

Test-time regression: a unifying framework for designing sequence models with associative memory

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

Natural Compression for Distributed Deep Learning

Unsupervised Learning by Competing Hidden Units

Contrastive Code Representation Learning

Optimal computational and statistical rates of convergence for sparse nonconvex learning problems

The HSIC Bottleneck: Deep Learning without Back-Propagation

Douglas-Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems

Robustness of Graph Neural Networks at Scale

The Inductive Bias of Quantum Kernels

Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching

A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization

Deep Multimodal Subspace Clustering Networks

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

The group fused Lasso for multiple change-point detection

The Emergence of Spectral Universality in Deep Networks

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma

Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification

Graphical-model based estimation and inference for differential privacy

Provably Consistent Partial-Label Learning

Practical Efficiency of Muon for Pretraining

Emotion in Reinforcement Learning Agents and Robots: A Survey

Fairwashing: the risk of rationalization

Insights into LSTM Fully Convolutional Networks for Time Series Classification

Modeling Information Propagation with Survival Theory

Distributed Online Convex Optimization with Time-Varying Coupled Inequality Constraints

An Evaluation of the Human-Interpretability of Explanation

TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

The Synthesizability of Molecules Proposed by Generative Models

Federated Learning of a Mixture of Global and Local Models

Multi-source Distilling Domain Adaptation

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

Variational Autoencoders and Nonlinear ICA: A Unifying Framework

Improving Variational Auto-Encoders using Householder Flow

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks

A Fusion Approach for Efficient Human Skin Detection

Graph Neural Ordinary Differential Equations

Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares

Randomized Sketches of Convex Programs with Sharp Guarantees

A Retrieve-and-Edit Framework for Predicting Structured Outputs

Multilingual Topic Models for Unaligned Text

The Multiscale Laplacian Graph Kernel

Normalizing Flows on Tori and Spheres

Randomized sketches for kernels: Fast and optimal non-parametric regression

How do infinite width bounded norm networks look in function space?

Avoiding Latent Variable Collapse With Generative Skip Models

Learning Controllable Fair Representations

C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework