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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4,681 to 4,740 of 6,792

A unifying view for performance measures in multi-class prediction
Giuseppe Jurman, Cesare Furlanello
stat.MLarXiv:1008.2908v12010
FairGAN: Fairness-aware Generative Adversarial Networks
Depeng Xu, Shuhan Yuan, Lu Zhang +1
cs.LGcs.CYstat.MLarXiv:1805.11202v12018
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks
Ying Zhang, Mohammad Pezeshki, Philemon Brakel +3
cs.CLcs.LGstat.MLarXiv:1701.02720v12017
Information Leakage in Embedding Models
Congzheng Song, Ananth Raghunathan
cs.LGcs.CLcs.CRarXiv:2004.00053v22020
The Why and How of Nonnegative Matrix Factorization
Nicolas Gillis
stat.MLcs.IRcs.LGarXiv:1401.5226v22014
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
Hippolyt Ritter, Aleksandar Botev, David Barber
stat.MLcs.LGarXiv:1805.07810v12018
Multiple Futures Prediction
Yichuan Charlie Tang, Ruslan Salakhutdinov
cs.LGcs.CVcs.MAarXiv:1911.00997v22019
Permutation Invariant Graph Generation via Score-Based Generative Modeling
Chenhao Niu, Yang Song, Jiaming Song +3
cs.LGstat.MLarXiv:2003.00638v12020
Gradient Matching for Domain Generalization
Yuge Shi, Jeffrey Seely, Philip H. S. Torr +4
cs.LGstat.MLarXiv:2104.09937v32021
On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization
Xiangyi Chen, Sijia Liu, Ruoyu Sun +1
cs.LGmath.OCstat.MLarXiv:1808.02941v22018
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil
math.OCcs.LGstat.MLarXiv:1901.08511v42019
Exploring the Landscape of Spatial Robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras +2
cs.LGcs.CVcs.NEarXiv:1712.02779v42017
Decentralized Federated Averaging
Tao Sun, Dongsheng Li, Bao Wang
cs.DCstat.MLarXiv:2104.11375v12021
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen +4
cs.LGcs.AIstat.MLarXiv:1906.02589v12019
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Chao Shang, Jie Chen, Jinbo Bi
cs.LGstat.MLarXiv:2101.06861v32021
Attention-based Graph Neural Network for Semi-supervised Learning
Kiran K. Thekumparampil, Chong Wang, Sewoong Oh +1
stat.MLcs.AIcs.LGarXiv:1803.03735v12018
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt +1
cs.LGmath.OCstat.MLarXiv:1207.4747v42012
Why ResNet Works? Residuals Generalize
Fengxiang He, Tongliang Liu, Dacheng Tao
stat.MLcs.LGarXiv:1904.01367v12019
Enhanced Membership Inference Attacks against Machine Learning Models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda +2
cs.LGcs.CRstat.MLarXiv:2111.09679v42021
Identifying Mislabeled Data using the Area Under the Margin Ranking
Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg +1
cs.LGcs.CVstat.MLarXiv:2001.10528v42020
ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
Sudipto Mukherjee, Himanshu Asnani, Eugene Lin +1
cs.LGstat.MLarXiv:1809.03627v22018
Generalization Properties of Learning with Random Features
Alessandro Rudi, Lorenzo Rosasco
stat.MLcs.LGarXiv:1602.04474v52016
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss
Lenaic Chizat, Francis Bach
math.OCcs.LGstat.MLarXiv:2002.04486v42020
GRAS: Guided Reduced-Variance Proposals and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion
Kwanyoung Kim
cs.LGcs.CEq-bio.QMarXiv:2608.26585v12026
catch22: CAnonical Time-series CHaracteristics
Carl H Lubba, Sarab S Sethi, Philip Knaute +3
cs.IRcs.LGstat.MLarXiv:1901.10200v22019
Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi
cs.CVstat.MLarXiv:1803.10082v12018
When Is the Sharp Covariance Envelope Tight? Feature-Only Geometry for Volume-Sampled Least Squares
Kihun Rhee
cs.LGstat.MLarXiv:2608.26877v12026
CheXclusion: Fairness gaps in deep chest X-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott +2
cs.CVcs.AIcs.LGarXiv:2003.00827v22020
On efficiency gains via augmenting a tiny sample with a massive auxiliary sample
Yen-Chi Chen
stat.MEmath.STstat.MLarXiv:2608.26610v12026
A Unified Descriptive-Complexity Framework for Model Selection under Correlated Designs
Yanhang Zhang, Wei Liu, Yuhong Yang
stat.MLcs.LGarXiv:2608.26618v12026
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
stat.MLcs.LGarXiv:1704.07433v42017
Meta-Learning Representations for Continual Learning
Khurram Javed, Martha White
cs.LGcs.AIstat.MLarXiv:1905.12588v22019
Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow
Gang Wang, Georgios B. Giannakis, Yonina C. Eldar
stat.MLcs.ITmath.OCarXiv:1605.08285v52016
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
Mauricio Sadinle, Jing Lei, Larry Wasserman
stat.MEcs.LGstat.MLarXiv:1609.00451v22016
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Puneet Mangla, Mayank Singh, Abhishek Sinha +3
cs.LGcs.CVstat.MLarXiv:1907.12087v42019
Interpretable Deep Neural Networks for Single-Trial EEG Classification
Irene Sturm, Sebastian Bach, Wojciech Samek +1
cs.NEstat.MLarXiv:1604.08201v12016
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
Wei Xiong, Hanze Dong, Chenlu Ye +5
cs.LGcs.AIstat.MLarXiv:2312.11456v42023
Fair Inference On Outcomes
Razieh Nabi, Ilya Shpitser
stat.MLarXiv:1705.10378v42017
Analysis of classifiers' robustness to adversarial perturbations
Alhussein Fawzi, Omar Fawzi, Pascal Frossard
cs.LGcs.CVstat.MLarXiv:1502.02590v42015
Supersparse Linear Integer Models for Optimized Medical Scoring Systems
Berk Ustun, Cynthia Rudin
stat.MLcs.DMcs.LGarXiv:1502.04269v32015
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Xiangyu Zhao, Liang Zhang, Zhuoye Ding +3
cs.IRcs.LGstat.MLarXiv:1802.06501v32018
Stochastic Optimization with Importance Sampling
Peilin Zhao, Tong Zhang
stat.MLcs.LGarXiv:1401.2753v22014
Laplacian Support Vector Machines Trained in the Primal
Stefano Melacci, Mikhail Belkin
stat.MLarXiv:0909.5422v12009
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Tian Zhou, Ziqing Ma, Xue wang +5
cs.LGstat.MLarXiv:2205.08897v42022
ATOMO: Communication-efficient Learning via Atomic Sparsification
Hongyi Wang, Scott Sievert, Zachary Charles +3
stat.MLcs.DCcs.LGarXiv:1806.04090v32018
Transfer Learning with Dynamic Adversarial Adaptation Network
Chaohui Yu, Jindong Wang, Yiqiang Chen +1
cs.LGstat.MLarXiv:1909.08184v12019
Hierarchical Federated Learning Across Heterogeneous Cellular Networks
Mehdi Salehi Heydar Abad, Emre Ozfatura, Deniz Gunduz +1
cs.LGcs.DCcs.ITarXiv:1909.02362v12019
Accelerated, Parallel and Proximal Coordinate Descent
Olivier Fercoq, Peter Richtárik
math.OCcs.DCmath.NAarXiv:1312.5799v22013
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
Zhewei Yao, Amir Gholami, Sheng Shen +3
cs.LGmath.NAstat.MLarXiv:2006.00719v32020
Sorting out Lipschitz function approximation
Cem Anil, James Lucas, Roger Grosse
cs.LGstat.MLarXiv:1811.05381v22018
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
Cheng Ju, Aurélien Bibaut, Mark J. van der Laan
stat.MLcs.CVcs.LGarXiv:1704.01664v12017
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg +22
stat.MLcs.CLcs.MSarXiv:1701.03980v12017
Distributed Gaussian Processes
Marc Peter Deisenroth, Jun Wei Ng
stat.MLarXiv:1502.02843v32015
A Survey on Negative Transfer
Wen Zhang, Lingfei Deng, Lei Zhang +1
cs.LGcs.CVstat.MLarXiv:2009.00909v42020
Graph-Bert: Only Attention is Needed for Learning Graph Representations
Jiawei Zhang, Haopeng Zhang, Congying Xia +1
cs.LGcs.NEstat.MLarXiv:2001.05140v22020
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein +2
stat.MLcs.LGarXiv:1806.05393v22018
Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
Michael Gastegger, Jörg Behler, Philipp Marquetand
physics.chem-phphysics.bio-phstat.MLarXiv:1705.05907v12017
Attributed Network Embedding for Learning in a Dynamic Environment
Jundong Li, Harsh Dani, Xia Hu +3
cs.SIcs.LGstat.MLarXiv:1706.01860v22017
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Huan Zhang, Hongge Chen, Chaowei Xiao +5
cs.LGcs.CRstat.MLarXiv:1906.06316v22019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu +2
stat.MLcs.LGmath.OCarXiv:1905.00877v62019

Machine Learning (stat)

A unifying view for performance measures in multi-class prediction

FairGAN: Fairness-aware Generative Adversarial Networks

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

Information Leakage in Embedding Models

The Why and How of Nonnegative Matrix Factorization

Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

Multiple Futures Prediction

Permutation Invariant Graph Generation via Score-Based Generative Modeling

Gradient Matching for Domain Generalization

On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization

A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach

Exploring the Landscape of Spatial Robustness

Decentralized Federated Averaging

Flexibly Fair Representation Learning by Disentanglement

Discrete Graph Structure Learning for Forecasting Multiple Time Series

Attention-based Graph Neural Network for Semi-supervised Learning

Block-Coordinate Frank-Wolfe Optimization for Structural SVMs

Why ResNet Works? Residuals Generalize

Enhanced Membership Inference Attacks against Machine Learning Models

Identifying Mislabeled Data using the Area Under the Margin Ranking

ClusterGAN : Latent Space Clustering in Generative Adversarial Networks

Generalization Properties of Learning with Random Features

Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss

GRAS: Guided Reduced-Variance Proposals and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion

catch22: CAnonical Time-series CHaracteristics

Efficient parametrization of multi-domain deep neural networks

When Is the Sharp Covariance Envelope Tight? Feature-Only Geometry for Volume-Sampled Least Squares

CheXclusion: Fairness gaps in deep chest X-ray classifiers

On efficiency gains via augmenting a tiny sample with a massive auxiliary sample

A Unified Descriptive-Complexity Framework for Model Selection under Correlated Designs

Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples

Meta-Learning Representations for Continual Learning

Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow

Least Ambiguous Set-Valued Classifiers with Bounded Error Levels

Charting the Right Manifold: Manifold Mixup for Few-shot Learning

Interpretable Deep Neural Networks for Single-Trial EEG Classification

Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

Fair Inference On Outcomes

Analysis of classifiers' robustness to adversarial perturbations

Supersparse Linear Integer Models for Optimized Medical Scoring Systems

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

Stochastic Optimization with Importance Sampling

Laplacian Support Vector Machines Trained in the Primal

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

ATOMO: Communication-efficient Learning via Atomic Sparsification

Transfer Learning with Dynamic Adversarial Adaptation Network

Hierarchical Federated Learning Across Heterogeneous Cellular Networks

Accelerated, Parallel and Proximal Coordinate Descent

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning

Sorting out Lipschitz function approximation

The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification

DyNet: The Dynamic Neural Network Toolkit

Distributed Gaussian Processes

A Survey on Negative Transfer

Graph-Bert: Only Attention is Needed for Learning Graph Representations

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra

Attributed Network Embedding for Learning in a Dynamic Environment

Towards Stable and Efficient Training of Verifiably Robust Neural Networks

You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle