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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6,841 to 6,900 of 9,719

Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Xiang Wei, Boqing Gong, Zixia Liu +2
cs.CVcs.LGstat.MLarXiv:1803.01541v12018
SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss +1
cs.LGcs.AIstat.MLarXiv:2603.05483v12026
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
cs.LGcs.AIcs.MAarXiv:1812.11794v22018
Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves
Sohail, Sarkar, Shakuntala Baichoo
cs.LGcs.CLmath.STarXiv:2609.40190v12026
Practical Contextual Bandits with Regression Oracles
Dylan J. Foster, Alekh Agarwal, Miroslav Dudík +2
cs.LGstat.MLarXiv:1803.01088v12018
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
David Duvenaud, James Robert Lloyd, Roger Grosse +2
stat.MLcs.LGstat.MEarXiv:1302.4922v42013
(More) Efficient Reinforcement Learning via Posterior Sampling
Ian Osband, Daniel Russo, Benjamin Van Roy
stat.MLcs.LGarXiv:1306.0940v52013
A Survey on Distributed Machine Learning
Joost Verbraeken, Matthijs Wolting, Jonathan Katzy +3
cs.LGcs.DCstat.MLarXiv:1912.09789v12019
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Ossama Ahmed, Frederik Träuble, Anirudh Goyal +5
cs.ROcs.LGstat.MLarXiv:2010.04296v22020
The Geometry of Randomized Smoothing on Feasible Sets
Syed Izhan Khilji, Alireza Furutanpey, Schahram Dustdar
cs.LGstat.MLarXiv:2609.39497v12026
Infinite Mixture Prototypes for Few-Shot Learning
Kelsey R. Allen, Evan Shelhamer, Hanul Shin +1
cs.LGstat.MLarXiv:1902.04552v12019
Adversarial Feature Selection against Evasion Attacks
Fei Zhang, Patrick P. K. Chan, Battista Biggio +2
cs.LGcs.CRstat.MLarXiv:2005.12154v12020
Unifying distillation and privileged information
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf +1
stat.MLcs.LGarXiv:1511.03643v32015
Root Mean Square Layer Normalization
Biao Zhang, Rico Sennrich
cs.LGcs.CLstat.MLarXiv:1910.07467v12019
Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain +4
cs.LGstat.MLarXiv:1905.11485v22019
Q-Edge: Symmetry-Reduced Quantum Simulation of Structured Extreme Dependence
Hongrui Zhang, Paolo Recchia, Ying Chen
quant-phstat.MLarXiv:2609.03706v12026
Chainer: A Deep Learning Framework for Accelerating the Research Cycle
Seiya Tokui, Ryosuke Okuta, Takuya Akiba +7
cs.LGcs.CVcs.DCarXiv:1908.00213v12019
Adversarially Robust Generalization Just Requires More Unlabeled Data
Runtian Zhai, Tianle Cai, Di He +4
cs.LGstat.MLarXiv:1906.00555v22019
Sampling Permutations for Shapley Value Estimation
Rory Mitchell, Joshua Cooper, Eibe Frank +1
stat.MLcs.LGmath.COarXiv:2104.12199v22021
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Tim Pearce, Mohamed Zaki, Alexandra Brintrup +1
stat.MLarXiv:1802.07167v32018
Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline
Zhiguang Wang, Weizhong Yan, Tim Oates
cs.LGcs.NEstat.MLarXiv:1611.06455v42016
An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists
Frédéric Chazal, Bertrand Michel
math.STcs.LGmath.ATarXiv:1710.04019v22017
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
Zhiyong Cui, Kristian Henrickson, Ruimin Ke +2
cs.LGstat.MLarXiv:1802.07007v32018
Non-asymptotic Identification of LTI Systems from a Single Trajectory
Samet Oymak, Necmiye Ozay
cs.LGeess.SYmath.OCarXiv:1806.05722v22018
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot +3
stat.MLcs.CRcs.LGarXiv:1705.07204v52017
Learning from Noisy Labels with Deep Neural Networks: A Survey
Hwanjun Song, Minseok Kim, Dongmin Park +2
cs.LGcs.CVstat.MLarXiv:2007.08199v72020
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Thang Doan, Mehdi Bennani, Bogdan Mazoure +2
cs.LGcs.AIstat.MLarXiv:2010.04003v22020
An overview of deep learning in medical imaging focusing on MRI
Alexander Selvikvåg Lundervold, Arvid Lundervold
cs.CVcs.LGstat.MLarXiv:1811.10052v22018
Traversing Knowledge Graphs in Vector Space
Kelvin Guu, John Miller, Percy Liang
cs.CLcs.AIcs.DBarXiv:1506.01094v22015
Z-Forcing: Training Stochastic Recurrent Networks
Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté +2
stat.MLcs.LGarXiv:1711.05411v22017
f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignment
Rajdeep Haldar, Lantao Mei, Guang Lin +2
cs.LGstat.MLarXiv:2602.05946v32026
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Sergei Popov, Stanislav Morozov, Artem Babenko
cs.LGstat.MLarXiv:1909.06312v22019
Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang, Kun Xu, Chao Du +2
cs.LGstat.MLarXiv:1901.08846v32019
Robust Detection of LLM-Generated Text under Contamination
Jiaxun Li, Saptarshi Chakraborty, Ambuj Tewari
stat.MLcs.LGarXiv:2609.29935v12026
Latent ODEs for Irregularly-Sampled Time Series
Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
cs.LGstat.MLarXiv:1907.03907v12019
VIGAN: Missing View Imputation with Generative Adversarial Networks
Chao Shang, Aaron Palmer, Jiangwen Sun +3
cs.CVstat.MLarXiv:1708.06724v52017
Learning ReLUs via Gradient Descent
Mahdi Soltanolkotabi
cs.LGcs.ITmath.OCarXiv:1705.04591v22017
Subgraph Matching Kernels for Attributed Graphs
Nils Kriege, Petra Mutzel
cs.LGstat.MLarXiv:1206.6483v12012
Efficient Evaluation of LLM Performance with Statistical Guarantees
Skyler Wu, Yash Nair, Emmanuel J. Candès
stat.MLcs.LGarXiv:2601.20251v32026
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
Daniel Liu, Ronald Yu, Hao Su
cs.CVcs.CRcs.LGarXiv:1901.03006v42019
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
Lin Xiao, Tong Zhang
math.OCstat.MLarXiv:1403.4699v12014
Efficient Estimation of Mutual Information for Strongly Dependent Variables
Shuyang Gao, Greg Ver Steeg, Aram Galstyan
cs.ITphysics.data-anstat.MLarXiv:1411.2003v32014
The UCR Time Series Archive
Hoang Anh Dau, Anthony Bagnall, Kaveh Kamgar +5
cs.LGstat.MLarXiv:1810.07758v22018
Transformers without Tears: Improving the Normalization of Self-Attention
Toan Q. Nguyen, Julian Salazar
cs.CLcs.LGstat.MLarXiv:1910.05895v22019
Large Classification-Risk-Optional Label Acquisition
F. Setoudehtanzangi, Geoffrey J. McLachlan
stat.MLcs.LGarXiv:2609.06873v12026
Provable Filter Pruning for Efficient Neural Networks
Lucas Liebenwein, Cenk Baykal, Harry Lang +2
cs.LGstat.MLarXiv:1911.07412v22019
Optimal Tradeoffs Between Network Size and Parameter Magnitude in Neural Approximation and Minimax Regression
Baicheng Li, Zuowei Shen, Haizhao Yang +1
stat.MLcs.LGarXiv:2609.25710v12026
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
Jongsoo Park, Maxim Naumov, Protonu Basu +25
cs.LGstat.MLarXiv:1811.09886v22018
Drug cell line interaction prediction
Pengfei Liu
q-bio.QMcs.LGstat.MLarXiv:1812.11178v12018
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
Hongliang Dai, Yangqiu Song
cs.CLcs.LGstat.MLarXiv:1907.03750v12019
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
Mikhail Belkin, Daniel Hsu, Partha Mitra
stat.MLcond-mat.stat-mechcs.LGarXiv:1806.05161v32018
Stabilizing Transformer Training by Preventing Attention Entropy Collapse
Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin +5
cs.LGcs.AIcs.CLarXiv:2303.06296v22023
The Big Data Bootstrap
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar +1
cs.LGstat.MLarXiv:1206.6415v12012
Characterizing and Avoiding Negative Transfer
Zirui Wang, Zihang Dai, Barnabás Póczos +1
cs.LGstat.MLarXiv:1811.09751v42018
Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?
Nitin Bansal, Xiaohan Chen, Zhangyang Wang
cs.LGcs.CVstat.MLarXiv:1810.09102v12018
Challenges in Training PINNs: A Loss Landscape Perspective
Pratik Rathore, Weimu Lei, Zachary Frangella +2
cs.LGmath.OCstat.MLarXiv:2402.01868v22024
The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction
Martin Gauch, Juliane Mai, Jimmy Lin
cs.LGstat.MLarXiv:1911.07249v32019
Physics Informed Extreme Learning Machine (PIELM) -- A rapid method for the numerical solution of partial differential equations
Vikas Dwivedi, Balaji Srinivasan
cs.LGphysics.comp-phstat.MLarXiv:1907.03507v12019
Learning to Decompose and Disentangle Representations for Video Prediction
Jun-Ting Hsieh, Bingbin Liu, De-An Huang +2
cs.LGcs.CVstat.MLarXiv:1806.04166v22018
Simplicial Neural Networks
Stefania Ebli, Michaël Defferrard, Gard Spreemann
cs.LGmath.ATstat.MLarXiv:2010.03633v22020

Machine Learning (stat)

Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect

SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis

Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications

Cheap to Draw, Expensive to Trust: Certifying Test-Time Scaling Curves

Practical Contextual Bandits with Regression Oracles

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

(More) Efficient Reinforcement Learning via Posterior Sampling

A Survey on Distributed Machine Learning

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

The Geometry of Randomized Smoothing on Feasible Sets

Infinite Mixture Prototypes for Few-Shot Learning

Adversarial Feature Selection against Evasion Attacks

Unifying distillation and privileged information

Root Mean Square Layer Normalization

Representation Learning for Dynamic Graphs: A Survey

Q-Edge: Symmetry-Reduced Quantum Simulation of Structured Extreme Dependence

Chainer: A Deep Learning Framework for Accelerating the Research Cycle

Adversarially Robust Generalization Just Requires More Unlabeled Data

Sampling Permutations for Shapley Value Estimation

High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach

Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Non-asymptotic Identification of LTI Systems from a Single Trajectory

Ensemble Adversarial Training: Attacks and Defenses

Learning from Noisy Labels with Deep Neural Networks: A Survey

A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix

An overview of deep learning in medical imaging focusing on MRI

Traversing Knowledge Graphs in Vector Space

Z-Forcing: Training Stochastic Recurrent Networks

f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignment

Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

Improving Adversarial Robustness via Promoting Ensemble Diversity

Robust Detection of LLM-Generated Text under Contamination

Latent ODEs for Irregularly-Sampled Time Series

VIGAN: Missing View Imputation with Generative Adversarial Networks

Learning ReLUs via Gradient Descent

Subgraph Matching Kernels for Attributed Graphs

Efficient Evaluation of LLM Performance with Statistical Guarantees

Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

A Proximal Stochastic Gradient Method with Progressive Variance Reduction

Efficient Estimation of Mutual Information for Strongly Dependent Variables

The UCR Time Series Archive

Transformers without Tears: Improving the Normalization of Self-Attention

Large Classification-Risk-Optional Label Acquisition

Provable Filter Pruning for Efficient Neural Networks

Optimal Tradeoffs Between Network Size and Parameter Magnitude in Neural Approximation and Minimax Regression

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

Drug cell line interaction prediction

Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision

Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate

Stabilizing Transformer Training by Preventing Attention Entropy Collapse

The Big Data Bootstrap

Characterizing and Avoiding Negative Transfer

Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?

Challenges in Training PINNs: A Loss Landscape Perspective

The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction

Physics Informed Extreme Learning Machine (PIELM) -- A rapid method for the numerical solution of partial differential equations

Learning to Decompose and Disentangle Representations for Video Prediction

Simplicial Neural Networks