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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3,001 to 3,060 of 6,792

To be Robust or to be Fair: Towards Fairness in Adversarial Training
Han Xu, Xiaorui Liu, Yaxin Li +2
cs.LGstat.MLarXiv:2010.06121v22020
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models
Cassidy Laidlaw, Sahil Singla, Soheil Feizi
cs.LGcs.CVstat.MLarXiv:2006.12655v42020
Fully Neural Network based Model for General Temporal Point Processes
Takahiro Omi, Naonori Ueda, Kazuyuki Aihara
cs.LGstat.MLarXiv:1905.09690v32019
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets
Corentin Hardy, Erwan Le Merrer, Bruno Sericola
cs.LGstat.MLarXiv:1811.03850v22018
Off-policy reinforcement learning for $ H_\infty $ control design
Biao Luo, Huai-Ning Wu, Tingwen Huang
eess.SYcs.LGmath.OCarXiv:1311.6107v32013
Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems
Mohsen Shahhosseini, Guiping Hu, Hieu Pham
stat.MLcs.LGstat.MEarXiv:1908.05287v62019
Graph signal processing for machine learning: A review and new perspectives
Xiaowen Dong, Dorina Thanou, Laura Toni +2
cs.LGcs.SIeess.SParXiv:2007.16061v12020
Recurrent Environment Simulators
Silvia Chiappa, Sébastien Racaniere, Daan Wierstra +1
cs.AIcs.LGstat.MLarXiv:1704.02254v22017
Decentralized Federated Learning: A Segmented Gossip Approach
Chenghao Hu, Jingyan Jiang, Zhi Wang
cs.LGcs.DCcs.NIarXiv:1908.07782v12019
Noisy Natural Gradient as Variational Inference
Guodong Zhang, Shengyang Sun, David Duvenaud +1
cs.LGstat.MLarXiv:1712.02390v22017
Testing for Outliers with Conformal p-values
Stephen Bates, Emmanuel Candès, Lihua Lei +2
stat.MEmath.STstat.MLarXiv:2104.08279v32021
Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices
Yudong Chen, Jiaming Xu
stat.MLmath.STarXiv:1402.1267v32014
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Pengchao Han, Shiqiang Wang, Kin K. Leung
cs.LGcs.DCmath.OCarXiv:2001.04756v32020
BRP-NAS: Prediction-based NAS using GCNs
Łukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah +3
cs.LGeess.SPstat.MLarXiv:2007.08668v42020
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
Maziar Raissi
stat.MLcs.LGeess.SYarXiv:1804.07010v12018
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
Dan Hendrycks, Thomas G. Dietterich
cs.LGcs.AIcs.CVarXiv:1807.01697v52018
Variance Reduction in SGD by Distributed Importance Sampling
Guillaume Alain, Alex Lamb, Chinnadhurai Sankar +2
stat.MLcs.LGarXiv:1511.06481v72015
Feature Selection via Regularized Trees
Houtao Deng, George Runger
cs.LGstat.MEstat.MLarXiv:1201.1587v32012
ProMP: Proximal Meta-Policy Search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera +2
cs.LGstat.MLarXiv:1810.06784v42018
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake +1
cs.LGcs.CLcs.CRarXiv:1910.08902v12019
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
Remi Tachet, Han Zhao, Yu-Xiang Wang +1
cs.LGcs.AIstat.MLarXiv:2003.04475v32020
A Theory of Usable Information Under Computational Constraints
Yilun Xu, Shengjia Zhao, Jiaming Song +2
cs.LGstat.MLarXiv:2002.10689v12020
The Singular Values of Convolutional Layers
Hanie Sedghi, Vineet Gupta, Philip M. Long
cs.LGcs.AIstat.MLarXiv:1805.10408v22018
Precision-Recall Curve (PRC) Classification Trees
Jiaju Miao, Wei Zhu
stat.MLcs.LGarXiv:2011.07640v12020
Real-time Power System State Estimation and Forecasting via Deep Neural Networks
Liang Zhang, Gang Wang, Georgios B. Giannakis
cs.LGstat.MLarXiv:1811.06146v22018
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi +1
cs.LGcs.DCstat.MLarXiv:1910.13598v22019
Marginal Coordinate Test for Fréchet Regression with Random Objects
Jiaye Chen, Rui Qiu, Roulin Wang +1
stat.MEstat.MLarXiv:2608.30644v12026
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu +2
eess.AScs.LGcs.SDarXiv:1908.03299v12019
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
Ting Chen, Yizhou Sun
cs.LGcs.AIcs.IRarXiv:1612.02814v22016
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Nikolaus Mayer, Eddy Ilg, Philipp Fischer +4
cs.CVstat.MLarXiv:1801.06397v32018
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
Gitta Kutyniok, Philipp Petersen, Mones Raslan +1
math.NAcs.LGmath.FAarXiv:1904.00377v32019
Pruning Neural Networks at Initialization: Why are We Missing the Mark?
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy +1
cs.LGcs.CVcs.NEarXiv:2009.08576v22020
Fourier Spectrum Discrepancies in Deep Network Generated Images
Tarik Dzanic, Karan Shah, Freddie Witherden
eess.IVcs.LGstat.MLarXiv:1911.06465v32019
Detecting Statistical Interactions from Neural Network Weights
Michael Tsang, Dehua Cheng, Yan Liu
stat.MLcs.LGarXiv:1705.04977v42017
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks
Tengyuan Liang, James Stokes
stat.MLcs.GTcs.LGarXiv:1802.06132v22018
Student-t Processes as Alternatives to Gaussian Processes
Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani
stat.MLcs.AIcs.LGarXiv:1402.4306v22014
An optimal randomized incremental gradient method
Guanghui Lan, Yi Zhou
math.OCcs.CCstat.MLarXiv:1507.02000v32015
Statistical consistency and asymptotic normality for high-dimensional robust M-estimators
Po-Ling Loh
math.STcs.ITstat.MLarXiv:1501.00312v12015
Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
Wei Chen, Andrew L Ferguson
physics.bio-phphysics.comp-phstat.MLarXiv:1801.00203v22017
Evasion and Hardening of Tree Ensemble Classifiers
Alex Kantchelian, J. D. Tygar, Anthony D. Joseph
cs.LGcs.CRstat.MLarXiv:1509.07892v22015
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
Andrei V. Konstantinov, Lev V. Utkin
cs.LGstat.MLarXiv:2010.07388v12020
Peer-to-peer Federated Learning on Graphs
Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi +1
cs.LGstat.MLarXiv:1901.11173v12019
Understanding Negative Sampling in Graph Representation Learning
Zhen Yang, Ming Ding, Chang Zhou +3
cs.LGstat.MLarXiv:2005.09863v22020
CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs
Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzman +1
cs.CLcs.LGstat.MLarXiv:1911.06154v22019
Introduction to Tensor Decompositions and their Applications in Machine Learning
Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann
stat.MLcs.LGarXiv:1711.10781v12017
Training Quantized Nets: A Deeper Understanding
Hao Li, Soham De, Zheng Xu +3
cs.LGcs.CVstat.MLarXiv:1706.02379v32017
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang, Igor Mordatch, Deepak Pathak
cs.LGcs.CVstat.MLarXiv:2007.04976v12020
Training Complex Models with Multi-Task Weak Supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon +3
stat.MLcs.LGarXiv:1810.02840v22018
Diffusion-based Image Translation using Disentangled Style and Content Representation
Gihyun Kwon, Jong Chul Ye
cs.CVcs.AIcs.LGarXiv:2209.15264v22022
Learning explanations that are hard to vary
Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto +2
cs.LGstat.MLarXiv:2009.00329v32020
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
Kumar Shridhar, Felix Laumann, Marcus Liwicki
cs.LGstat.MLarXiv:1901.02731v12019
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Andrey Malinin, Mark Gales
stat.MLcs.LGarXiv:1905.13472v22019
Graph Unlearning
Min Chen, Zhikun Zhang, Tianhao Wang +3
cs.LGcs.AIcs.CRarXiv:2103.14991v22021
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information
Peng Xu, Fred Roosta, Michael W. Mahoney
math.OCcs.CCcs.LGarXiv:1708.07164v42017
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Jinghui Chen, Dongruo Zhou, Yiqi Tang +3
cs.LGstat.MLarXiv:1806.06763v32018
Neural means and kernel corrections for operator learning
Yitzchak Shmalo
cs.LGmath.PRstat.MLarXiv:2609.00389v12026
Minibatch vs Local SGD for Heterogeneous Distributed Learning
Blake Woodworth, Kumar Kshitij Patel, Nathan Srebro
cs.LGmath.OCstat.MLarXiv:2006.04735v52020
Inductive Graph Neural Networks for Spatiotemporal Kriging
Yuankai Wu, Dingyi Zhuang, Aurelie Labbe +1
cs.LGstat.MLarXiv:2006.07527v22020
Generative Poisoning Attack Method Against Neural Networks
Chaofei Yang, Qing Wu, Hai Li +1
cs.CRcs.LGstat.MLarXiv:1703.01340v12017
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
Yangtao Wang, Xi Shen, Shell Hu +3
cs.CVstat.MLarXiv:2202.11539v22022

Machine Learning (stat)

To be Robust or to be Fair: Towards Fairness in Adversarial Training

Perceptual Adversarial Robustness: Defense Against Unseen Threat Models

Fully Neural Network based Model for General Temporal Point Processes

MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets

Off-policy reinforcement learning for $ H_\infty $ control design

Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems

Graph signal processing for machine learning: A review and new perspectives

Recurrent Environment Simulators

Decentralized Federated Learning: A Segmented Gossip Approach

Noisy Natural Gradient as Variational Inference

Testing for Outliers with Conformal p-values

Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices

Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach

BRP-NAS: Prediction-based NAS using GCNs

Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

Variance Reduction in SGD by Distributed Importance Sampling

Feature Selection via Regularized Trees

ProMP: Proximal Meta-Policy Search

Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift

A Theory of Usable Information Under Computational Constraints

The Singular Values of Convolutional Layers

Precision-Recall Curve (PRC) Classification Trees

Real-time Power System State Estimation and Forecasting via Deep Neural Networks

Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization

Marginal Coordinate Test for Fréchet Regression with Random Objects

ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification

What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?

A Theoretical Analysis of Deep Neural Networks and Parametric PDEs

Pruning Neural Networks at Initialization: Why are We Missing the Mark?

Fourier Spectrum Discrepancies in Deep Network Generated Images

Detecting Statistical Interactions from Neural Network Weights

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks

Student-t Processes as Alternatives to Gaussian Processes

An optimal randomized incremental gradient method

Statistical consistency and asymptotic normality for high-dimensional robust M-estimators

Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration

Evasion and Hardening of Tree Ensemble Classifiers

Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines

Peer-to-peer Federated Learning on Graphs

Understanding Negative Sampling in Graph Representation Learning

CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs

Introduction to Tensor Decompositions and their Applications in Machine Learning

Training Quantized Nets: A Deeper Understanding

One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

Training Complex Models with Multi-Task Weak Supervision

Diffusion-based Image Translation using Disentangled Style and Content Representation

Learning explanations that are hard to vary

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness

Graph Unlearning

Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks

Neural means and kernel corrections for operator learning

Minibatch vs Local SGD for Heterogeneous Distributed Learning

Inductive Graph Neural Networks for Spatiotemporal Kriging

Generative Poisoning Attack Method Against Neural Networks

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut