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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1,501 to 1,560 of 6,780

On the Turing Completeness of Modern Neural Network Architectures
Jorge Pérez, Javier Marinković, Pablo Barceló
cs.LGcs.FLstat.MLarXiv:1901.03429v12019
A General Framework for Constrained Bayesian Optimization using Information-based Search
José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams +2
stat.MLarXiv:1511.09422v22015
CONTRA: Conformal Prediction Region via Normalizing Flow Transformation
Zhenhan Fang, Aixin Tan, Jian Huang
stat.MLcs.LGarXiv:2605.08561v12026
The Unusual Effectiveness of Averaging in GAN Training
Yasin Yazıcı, Chuan-Sheng Foo, Stefan Winkler +3
stat.MLcs.CVcs.LGarXiv:1806.04498v22018
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions
Luyang Fang, Xiaowei Yu, Jiazhang Cai +23
cs.CLcs.LGstat.MLarXiv:2504.14772v22025
Early Visual Concept Learning with Unsupervised Deep Learning
Irina Higgins, Loic Matthey, Xavier Glorot +5
stat.MLcs.LGq-bio.NCarXiv:1606.05579v32016
Nonlinear Information Bottleneck
Artemy Kolchinsky, Brendan D. Tracey, David H. Wolpert
cs.ITcs.LGstat.MLarXiv:1705.02436v92017
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions
Zebin Yang, Aijun Zhang, Agus Sudjianto
stat.MLcs.LGstat.COarXiv:2003.07132v22020
Is Out-of-Distribution Detection Learnable?
Zhen Fang, Yixuan Li, Jie Lu +3
cs.LGstat.MLarXiv:2210.14707v32022
Diffusion Models for Robotic Manipulation: A Survey
Rosa Wolf, Yitian Shi, Sheng Liu +1
cs.ROstat.MLarXiv:2504.08438v32025
Wide Compression: Tensor Ring Nets
Wenqi Wang, Yifan Sun, Brian Eriksson +2
cs.LGcs.CVstat.MLarXiv:1802.09052v12018
Adaptive Neural Trees
Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander +2
cs.NEcs.CVcs.LGarXiv:1807.06699v52018
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Po-Sen Huang, Robert Stanforth, Johannes Welbl +5
cs.CLcs.CRcs.LGarXiv:1909.01492v22019
Improved Baselines with Representation Autoencoders
Jaskirat Singh, Boyang Zheng, Zongze Wu +3
cs.CVcs.AIcs.GRarXiv:2605.18324v22026
Simplicial Neural Networks
Stefania Ebli, Michaël Defferrard, Gard Spreemann
cs.LGmath.ATstat.MLarXiv:2010.03633v22020
Generalized and Scalable Optimal Sparse Decision Trees
Jimmy Lin, Chudi Zhong, Diane Hu +2
cs.LGstat.MLarXiv:2006.08690v42020
On Analog Gradient Descent Learning over Multiple Access Fading Channels
Tomer Sery, Kobi Cohen
cs.LGcs.ITstat.MLarXiv:1908.07463v12019
Firefly Monte Carlo: Exact MCMC with Subsets of Data
Dougal Maclaurin, Ryan P. Adams
stat.MLcs.LGstat.COarXiv:1403.5693v12014
Stability and Generalization of Graph Convolutional Neural Networks
Saurabh Verma, Zhi-Li Zhang
cs.LGcs.AIstat.MLarXiv:1905.01004v22019
TristouNet: Triplet Loss for Speaker Turn Embedding
Hervé Bredin
cs.SDstat.MLarXiv:1609.04301v32016
Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe +1
stat.MLcs.LGarXiv:2203.16437v32022
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate
Vitaly Feldman, Jan Vondrak
cs.LGcs.DSstat.MLarXiv:1902.10710v22019
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
Pushpendu Ghosh, Ariel Neufeld, Jajati Keshari Sahoo
cs.LGq-fin.STstat.MLarXiv:2004.10178v22020
On Symmetric and Asymmetric LSHs for Inner Product Search
Behnam Neyshabur, Nathan Srebro
stat.MLcs.DScs.IRarXiv:1410.5518v32014
Circulant Binary Embedding
Felix X. Yu, Sanjiv Kumar, Yunchao Gong +1
stat.MLcs.LGarXiv:1405.3162v12014
Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions
Xiaodong Li
cs.ITstat.MLarXiv:1104.1041v22011
A Finite Time Analysis of Two Time-Scale Actor Critic Methods
Yue Wu, Weitong Zhang, Pan Xu +1
cs.LGmath.OCstat.MLarXiv:2005.01350v32020
The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks
João Manoel Herrera Pinheiro, Suzana Vilas Boas de Oliveira, Thiago Henrique Segreto Silva +5
cs.LGstat.MLarXiv:2506.08274v52025
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Kun Xu, Lingfei Wu, Zhiguo Wang +3
cs.AIcs.CLcs.LGarXiv:1804.00823v42018
Meta Flow Maps enable scalable reward alignment
Peter Potaptchik, Adhi Saravanan, Abbas Mammadov +3
stat.MLcs.LGarXiv:2601.14430v22026
Pomegranate: fast and flexible probabilistic modeling in python
Jacob Schreiber
cs.AIcs.LGstat.MLarXiv:1711.00137v22017
MolecularRNN: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva +1
cs.LGcs.AIq-bio.MNarXiv:1905.13372v12019
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Zhao Chen, Xiaodong Wang
cs.LGeess.SPmath.OCarXiv:1812.07394v12018
Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation
Thomas Vandal, Evan Kodra, Auroop R Ganguly
stat.MLarXiv:1702.04018v12017
Not too little, not too much: a theoretical analysis of graph (over)smoothing
Nicolas Keriven
stat.MLcs.LGarXiv:2205.12156v22022
An Army of Me: Sockpuppets in Online Discussion Communities
Srijan Kumar, Justin Cheng, Jure Leskovec +1
cs.SIcs.CYphysics.soc-pharXiv:1703.07355v12017
A Framework for Evaluating Approximation Methods for Gaussian Process Regression
Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray
stat.MLcs.LGstat.COarXiv:1205.6326v22012
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
Namyong Park, Andrey Kan, Xin Luna Dong +2
cs.LGcs.IRstat.MLarXiv:1905.08865v22019
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
Brady Neal, Sarthak Mittal, Aristide Baratin +4
cs.LGstat.MLarXiv:1810.08591v42018
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +2
stat.MLcs.AIcs.CEarXiv:1706.06689v12017
Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations
Katie Matton, Robert Osazuwa Ness, John Guttag +1
cs.CLcs.AIcs.LGarXiv:2504.14150v22025
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems
Eirini Anthi, Lowri Williams, Matilda Rhode +2
cs.LGcs.CReess.SParXiv:2004.05005v12020
Graphon Neural Networks and the Transferability of Graph Neural Networks
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
cs.LGstat.MLarXiv:2006.03548v22020
A Smoothed Dual Approach for Variational Wasserstein Problems
Marco Cuturi, Gabriel Peyré
stat.MLmath.OCarXiv:1503.02533v22015
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Shyam A. Tailor, Javier Fernandez-Marques, Nicholas D. Lane
cs.LGstat.MLarXiv:2008.05000v32020
Proportionally Fair Clustering
Xingyu Chen, Brandon Fain, Liang Lyu +1
cs.LGcs.DScs.GTarXiv:1905.03674v32019
Unconstrained Monotonic Neural Networks
Antoine Wehenkel, Gilles Louppe
cs.LGcs.NEstat.MLarXiv:1908.05164v32019
Randomized Nonlinear Component Analysis
David Lopez-Paz, Suvrit Sra, Alex Smola +2
stat.MLcs.LGarXiv:1402.0119v22014
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi, Minkai Xu, Hongyu Guo +2
cs.LGstat.MLarXiv:2003.12725v32020
Flows for simultaneous manifold learning and density estimation
Johann Brehmer, Kyle Cranmer
stat.MLcs.LGarXiv:2003.13913v32020
Better Exploration with Optimistic Actor-Critic
Kamil Ciosek, Quan Vuong, Robert Loftin +1
stat.MLcs.LGarXiv:1910.12807v12019
InstaHide: Instance-hiding Schemes for Private Distributed Learning
Yangsibo Huang, Zhao Song, Kai Li +1
cs.CRcs.CCcs.DSarXiv:2010.02772v22020
Diffusion Models are Minimax Optimal Distribution Estimators
Kazusato Oko, Shunta Akiyama, Taiji Suzuki
stat.MLcs.LGarXiv:2303.01861v12023
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Ron Amit, Ron Meir
stat.MLcs.AIcs.LGarXiv:1711.01244v82017
Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms
Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu +1
q-bio.OTcs.LGstat.AParXiv:1908.06746v52019
The ALAMO approach to machine learning
Zachary T. Wilson, Nikolaos V. Sahinidis
cs.LGstat.MLarXiv:1705.10918v12017
Overfitting Mechanism and Avoidance in Deep Neural Networks
Shaeke Salman, Xiuwen Liu
cs.LGcs.NEstat.MLarXiv:1901.06566v12019
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
Peng Zheng, Travis Askham, Steven L. Brunton +2
stat.MLcs.LGmath.OCarXiv:1807.05411v42018
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

Machine Learning (stat)

On the Turing Completeness of Modern Neural Network Architectures

A General Framework for Constrained Bayesian Optimization using Information-based Search

CONTRA: Conformal Prediction Region via Normalizing Flow Transformation

The Unusual Effectiveness of Averaging in GAN Training

Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions

Early Visual Concept Learning with Unsupervised Deep Learning

Nonlinear Information Bottleneck

GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions

Is Out-of-Distribution Detection Learnable?

Diffusion Models for Robotic Manipulation: A Survey

Wide Compression: Tensor Ring Nets

Adaptive Neural Trees

Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation

Improved Baselines with Representation Autoencoders

Simplicial Neural Networks

Generalized and Scalable Optimal Sparse Decision Trees

On Analog Gradient Descent Learning over Multiple Access Fading Channels

Firefly Monte Carlo: Exact MCMC with Subsets of Data

Stability and Generalization of Graph Convolutional Neural Networks

TristouNet: Triplet Loss for Speaker Turn Embedding

Weakly supervised causal representation learning

High probability generalization bounds for uniformly stable algorithms with nearly optimal rate

Forecasting directional movements of stock prices for intraday trading using LSTM and random forests

On Symmetric and Asymmetric LSHs for Inner Product Search

Circulant Binary Embedding

Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions

A Finite Time Analysis of Two Time-Scale Actor Critic Methods

The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

Meta Flow Maps enable scalable reward alignment

Pomegranate: fast and flexible probabilistic modeling in python

MolecularRNN: Generating realistic molecular graphs with optimized properties

Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach

Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation

Not too little, not too much: a theoretical analysis of graph (over)smoothing

An Army of Me: Sockpuppets in Online Discussion Communities

A Framework for Evaluating Approximation Methods for Gaussian Process Regression

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

A Modern Take on the Bias-Variance Tradeoff in Neural Networks

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations

Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems

Graphon Neural Networks and the Transferability of Graph Neural Networks

A Smoothed Dual Approach for Variational Wasserstein Problems

Degree-Quant: Quantization-Aware Training for Graph Neural Networks

Proportionally Fair Clustering

Unconstrained Monotonic Neural Networks

Randomized Nonlinear Component Analysis

A Graph to Graphs Framework for Retrosynthesis Prediction

Flows for simultaneous manifold learning and density estimation

Better Exploration with Optimistic Actor-Critic

InstaHide: Instance-hiding Schemes for Private Distributed Learning

Diffusion Models are Minimax Optimal Distribution Estimators

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

Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms

The ALAMO approach to machine learning

Overfitting Mechanism and Avoidance in Deep Neural Networks

A Unified Framework for Sparse Relaxed Regularized Regression: SR3

Variational Federated Multi-Task Learning

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts