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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3,181 to 3,240 of 6,786

Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild
Kibok Lee, Kimin Lee, Jinwoo Shin +1
cs.CVcs.LGstat.MLarXiv:1903.12648v32019
Adversarial Continual Learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra +2
cs.LGcs.AIcs.CVarXiv:2003.09553v22020
Uniform Sampling for Matrix Approximation
Michael B. Cohen, Yin Tat Lee, Cameron Musco +3
cs.DScs.LGstat.MLarXiv:1408.5099v12014
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Bo Chang, Minmin Chen, Eldad Haber +1
stat.MLcs.LGarXiv:1902.09689v12019
Solving and Learning Nonlinear PDEs with Gaussian Processes
Yifan Chen, Bamdad Hosseini, Houman Owhadi +1
math.NAstat.MLarXiv:2103.12959v22021
Robust Estimation via Robust Gradient Estimation
Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan +1
stat.MLcs.AIcs.LGarXiv:1802.06485v22018
The NetHack Learning Environment
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller +4
cs.LGcs.AIcs.CLarXiv:2006.13760v22020
Global Optimality Guarantees For Policy Gradient Methods
Jalaj Bhandari, Daniel Russo
cs.LGstat.MLarXiv:1906.01786v32019
Invariant Rationalization
Shiyu Chang, Yang Zhang, Mo Yu +1
cs.LGcs.AIcs.CLarXiv:2003.09772v12020
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models
Cheolhyoung Lee, Kyunghyun Cho, Wanmo Kang
cs.LGstat.MLarXiv:1909.11299v22019
Learning to Search Better Than Your Teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal +2
cs.LGstat.MLarXiv:1502.02206v22015
Wasserstein Weisfeiler-Lehman Graph Kernels
Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López +2
cs.LGq-bio.MNstat.MLarXiv:1906.01277v22019
A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images
Yide Zhang, Yinhao Zhu, Evan Nichols +4
cs.CVcs.LGeess.IVarXiv:1812.10366v22018
E(n) Equivariant Normalizing Flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs +2
cs.LGphysics.chem-phstat.MLarXiv:2105.09016v42021
Interpretable and Efficient Heterogeneous Graph Convolutional Network
Yaming Yang, Ziyu Guan, Jianxin Li +3
cs.LGcs.SIstat.MLarXiv:2005.13183v32020
Bayesian optimization for materials design
Peter I. Frazier, Jialei Wang
stat.MLmath.OCarXiv:1506.01349v12015
An Empirical Evaluation of Similarity Measures for Time Series Classification
Joan Serrà, Josep Lluis Arcos
cs.LGcs.CVstat.MLarXiv:1401.3973v12014
Learning to Pivot with Adversarial Networks
Gilles Louppe, Michael Kagan, Kyle Cranmer
stat.MLcs.LGcs.NEarXiv:1611.01046v32016
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
Jianbo Chen, Le Song, Martin J. Wainwright +1
cs.LGstat.MLarXiv:1808.02610v12018
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks
Divyansh Kaushik, Zachary C. Lipton
cs.CLcs.AIcs.LGarXiv:1808.04926v22018
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Kaixiong Zhou, Xiao Huang, Yuening Li +3
cs.LGstat.MLarXiv:2006.06972v12020
Capsule Network Performance on Complex Data
Edgar Xi, Selina Bing, Yang Jin
stat.MLcs.LGarXiv:1712.03480v12017
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning
Anvith Thudi, Hengrui Jia, Ilia Shumailov +1
cs.LGcs.AIcs.CRarXiv:2110.11891v22021
Better Fine-Tuning by Reducing Representational Collapse
Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta +3
cs.LGcs.CLstat.MLarXiv:2008.03156v12020
Conditioning by adaptive sampling for robust design
David H. Brookes, Hahnbeom Park, Jennifer Listgarten
cs.LGstat.MLarXiv:1901.10060v92019
Neural Predictor for Neural Architecture Search
Wei Wen, Hanxiao Liu, Hai Li +3
cs.LGstat.MLarXiv:1912.00848v12019
Learning Optimal Resource Allocations in Wireless Systems
Mark Eisen, Clark Zhang, Luiz F. O. Chamon +2
cs.LGcs.NIstat.MLarXiv:1807.08088v32018
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks
Zhaoxian Wu, Qing Ling, Tianyi Chen +1
cs.LGcs.AIcs.CRarXiv:1912.12716v22019
Motivating the Rules of the Game for Adversarial Example Research
Justin Gilmer, Ryan P. Adams, Ian Goodfellow +2
cs.LGstat.MLarXiv:1807.06732v22018
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer +2
cs.LGstat.MLarXiv:1810.00821v42018
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
Jiaxi Tang, Ke Wang
cs.LGcs.IRstat.MLarXiv:1809.07428v12018
Conformal Prediction: a Unified Review of Theory and New Challenges
Matteo Fontana, Gianluca Zeni, Simone Vantini
cs.LGecon.EMstat.MEarXiv:2005.07972v22020
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
Zheyuan Hu, Khemraj Shukla, George Em Karniadakis +1
cs.LGcs.AImath.DSarXiv:2307.12306v62023
Adversarial Feature Selection against Evasion Attacks
Fei Zhang, Patrick P. K. Chan, Battista Biggio +2
cs.LGcs.CRstat.MLarXiv:2005.12154v12020
Improvements to deep convolutional neural networks for LVCSR
Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed +6
cs.LGcs.CLcs.NEarXiv:1309.1501v32013
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh +7
cs.LGcs.CVstat.MLarXiv:2306.04675v22023
Adversarial examples from computational constraints
Sébastien Bubeck, Eric Price, Ilya Razenshteyn
stat.MLcs.CCcs.LGarXiv:1805.10204v12018
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
Tuomas Kynkäänniemi, Miika Aittala, Tero Karras +3
cs.CVcs.AIcs.LGarXiv:2404.07724v22024
MetaPoison: Practical General-purpose Clean-label Data Poisoning
W. Ronny Huang, Jonas Geiping, Liam Fowl +2
cs.LGcs.AIcs.CRarXiv:2004.00225v22020
Survey of resampling techniques for improving classification performance in unbalanced datasets
Ajinkya More
stat.APcs.LGstat.MLarXiv:1608.06048v12016
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods
Nicolas Loizou, Peter Richtárik
math.OCcs.LGmath.NAarXiv:1712.09677v22017
Sparse and Imperceivable Adversarial Attacks
Francesco Croce, Matthias Hein
cs.LGcs.CRcs.CVarXiv:1909.05040v12019
Max-Sliced Wasserstein Distance and its use for GANs
Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun +6
cs.LGcs.CVstat.MLarXiv:1904.05877v22019
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Hongteng Xu, Dixin Luo, Lawrence Carin
cs.LGcs.SIstat.MLarXiv:1905.07645v52019
Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet +1
stat.MLcs.LGarXiv:2308.03686v32023
Reward-Free Exploration for Reinforcement Learning
Chi Jin, Akshay Krishnamurthy, Max Simchowitz +1
cs.LGstat.MLarXiv:2002.02794v12020
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Divyat Mahajan, Chenhao Tan, Amit Sharma
cs.LGcs.AIstat.MLarXiv:1912.03277v32019
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
Erik Nijkamp, Mitch Hill, Song-Chun Zhu +1
stat.MLcs.LGarXiv:1904.09770v42019
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui +2
cs.LGstat.MLarXiv:1904.11088v42019
Confounding Masquerading as Improvement: A Systematic Evaluation of Offline Reinforcement Learning for Stroke Antithrombotic Treatment in a 129,000-Patient Registry
Kihun Rhee
cs.LGstat.MLarXiv:2608.30442v12026
Neural Networks Fail to Learn Periodic Functions and How to Fix It
Liu Ziyin, Tilman Hartwig, Masahito Ueda
cs.LGstat.MLarXiv:2006.08195v22020
Policy Gradients with Variance Related Risk Criteria
Dotan Di Castro, Aviv Tamar, Shie Mannor
cs.LGcs.CYmath.OCarXiv:1206.6404v12012
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martinez, Martin Bertran, Guillermo Sapiro
stat.MLcs.LGarXiv:2011.01821v12020
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
Christopher Frye, Colin Rowat, Ilya Feige
stat.MLcs.AIcs.LGarXiv:1910.06358v32019
Adaptive Stress Testing for Autonomous Vehicles
Mark Koren, Saud Alsaif, Ritchie Lee +1
cs.ROcs.AIcs.LGarXiv:1902.01909v12019
Video Compression With Rate-Distortion Autoencoders
Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak +1
eess.IVcs.LGstat.MLarXiv:1908.05717v22019
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Nicholas R. Waytowich, Vernon Lawhern, Javier O. Garcia +4
cs.LGq-bio.NCstat.MLarXiv:1803.04566v22018
Nash Learning from Human Feedback
Rémi Munos, Michal Valko, Daniele Calandriello +14
stat.MLcs.AIcs.GTarXiv:2312.00886v42023
Better Theory for SGD in the Nonconvex World
Ahmed Khaled, Peter Richtárik
math.OCcs.LGstat.MLarXiv:2002.03329v32020
Variational Sequential Monte Carlo
Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath +1
stat.MLstat.COstat.MEarXiv:1705.11140v22017

Machine Learning (stat)

Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild

Adversarial Continual Learning

Uniform Sampling for Matrix Approximation

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Solving and Learning Nonlinear PDEs with Gaussian Processes

Robust Estimation via Robust Gradient Estimation

The NetHack Learning Environment

Global Optimality Guarantees For Policy Gradient Methods

Invariant Rationalization

Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models

Learning to Search Better Than Your Teacher

Wasserstein Weisfeiler-Lehman Graph Kernels

A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images

E(n) Equivariant Normalizing Flows

Interpretable and Efficient Heterogeneous Graph Convolutional Network

Bayesian optimization for materials design

An Empirical Evaluation of Similarity Measures for Time Series Classification

Learning to Pivot with Adversarial Networks

L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data

How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Capsule Network Performance on Complex Data

On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning

Better Fine-Tuning by Reducing Representational Collapse

Conditioning by adaptive sampling for robust design

Neural Predictor for Neural Architecture Search

Learning Optimal Resource Allocations in Wireless Systems

Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks

Motivating the Rules of the Game for Adversarial Example Research

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

Conformal Prediction: a Unified Review of Theory and New Challenges

Tackling the Curse of Dimensionality with Physics-Informed Neural Networks

Adversarial Feature Selection against Evasion Attacks

Improvements to deep convolutional neural networks for LVCSR

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

Adversarial examples from computational constraints

Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models

MetaPoison: Practical General-purpose Clean-label Data Poisoning

Survey of resampling techniques for improving classification performance in unbalanced datasets

Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods

Sparse and Imperceivable Adversarial Attacks

Max-Sliced Wasserstein Distance and its use for GANs

Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reward-Free Exploration for Reinforcement Learning

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs

Confounding Masquerading as Improvement: A Systematic Evaluation of Offline Reinforcement Learning for Stroke Antithrombotic Treatment in a 129,000-Patient Registry

Neural Networks Fail to Learn Periodic Functions and How to Fix It

Policy Gradients with Variance Related Risk Criteria

Minimax Pareto Fairness: A Multi Objective Perspective

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability

Adaptive Stress Testing for Autonomous Vehicles

Video Compression With Rate-Distortion Autoencoders

Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials

Nash Learning from Human Feedback

Better Theory for SGD in the Nonconvex World

Variational Sequential Monte Carlo