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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4,081 to 4,140 of 6,773

STC Antispoofing Systems for the ASVspoof2019 Challenge
Galina Lavrentyeva, Sergey Novoselov, Andzhukaev Tseren +3
cs.SDcs.CLcs.CRarXiv:1904.05576v12019
Bayesian Temporal Factorization for Multidimensional Time Series Prediction
Xinyu Chen, Lijun Sun
stat.MLcs.LGarXiv:1910.06366v22019
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Zhiyuan Li, Hong Liu, Denny Zhou +1
cs.LGcs.CCstat.MLarXiv:2402.12875v42024
Action Robust Reinforcement Learning and Applications in Continuous Control
Chen Tessler, Yonathan Efroni, Shie Mannor
cs.LGstat.MLarXiv:1901.09184v22019
Entangled Watermarks as a Defense against Model Extraction
Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran +1
cs.CRstat.MLarXiv:2002.12200v22020
Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent
Zeyuan Allen-Zhu, Lorenzo Orecchia
cs.DScs.LGmath.NAarXiv:1407.1537v52014
How Complex is your classification problem? A survey on measuring classification complexity
Ana C. Lorena, Luís P. F. Garcia, Jens Lehmann +2
cs.LGstat.MLarXiv:1808.03591v32018
Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning
R. Srikant, Lei Ying
cs.LGstat.MLarXiv:1902.00923v32019
Bag of Tricks for Adversarial Training
Tianyu Pang, Xiao Yang, Yinpeng Dong +2
cs.LGcs.CVstat.MLarXiv:2010.00467v32020
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
Erich Schubert, Peter J. Rousseeuw
cs.LGstat.MLarXiv:1810.05691v42018
Deep AutoRegressive Networks
Karol Gregor, Ivo Danihelka, Andriy Mnih +2
cs.LGstat.MLarXiv:1310.8499v22013
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
Peter Kairouz, Ziyu Liu, Thomas Steinke
cs.LGcs.DSstat.MLarXiv:2102.06387v42021
The Kolmogorov-Arnold representation theorem revisited
Johannes Schmidt-Hieber
cs.LGcs.NEstat.MLarXiv:2007.15884v22020
Explaining the Explainer: A First Theoretical Analysis of LIME
Damien Garreau, Ulrike von Luxburg
cs.LGstat.MLarXiv:2001.03447v22020
A Bayesian Perspective on Generalization and Stochastic Gradient Descent
Samuel L. Smith, Quoc V. Le
cs.LGcs.AIstat.MLarXiv:1710.06451v32017
Neural Expectation Maximization
Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber
cs.LGcs.NEstat.MLarXiv:1708.03498v22017
Learning Equations for Extrapolation and Control
Subham S. Sahoo, Christoph H. Lampert, Georg Martius
cs.LGstat.MLarXiv:1806.07259v12018
Fast Differentiable Sorting and Ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet +1
stat.MLcs.LGarXiv:2002.08871v22020
Learned Optimizers that Scale and Generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman +4
cs.LGcs.NEstat.MLarXiv:1703.04813v42017
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
James Townsend, Niklas Koep, Sebastian Weichwald
cs.MScs.LGmath.OCarXiv:1603.03236v42016
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
Yu Li, Tao Wang, Bingyi Kang +4
cs.CVcs.LGstat.MLarXiv:2006.10408v12020
Mean estimation and regression under heavy-tailed distributions--a survey
Gabor Lugosi, Shahar Mendelson
math.STcs.LGstat.MLarXiv:1906.04280v12019
Self-Imitation Learning
Junhyuk Oh, Yijie Guo, Satinder Singh +1
cs.LGcs.AIstat.MLarXiv:1806.05635v12018
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms
Ruoxi Jia, David Dao, Boxin Wang +6
cs.LGstat.MLarXiv:1908.08619v42019
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
Ke Sun, Zhouchen Lin, Zhanxing Zhu
cs.LGstat.MLarXiv:1902.11038v22019
MineRL: A Large-Scale Dataset of Minecraft Demonstrations
William H. Guss, Brandon Houghton, Nicholay Topin +4
cs.LGcs.AIcs.NEarXiv:1907.13440v12019
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Andrea Zanette, Emma Brunskill
cs.LGcs.AIstat.MLarXiv:1901.00210v42019
Auditing Black-box Models for Indirect Influence
Philip Adler, Casey Falk, Sorelle A. Friedler +4
stat.MLcs.LGarXiv:1602.07043v22016
Gradient Descent Happens in a Tiny Subspace
Guy Gur-Ari, Daniel A. Roberts, Ethan Dyer
cs.LGcs.AIstat.MLarXiv:1812.04754v12018
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le +2
cs.LGstat.MLarXiv:1806.02426v12018
Evolution-Guided Policy Gradient in Reinforcement Learning
Shauharda Khadka, Kagan Tumer
cs.LGcs.NEstat.MLarXiv:1805.07917v22018
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
Thomas Schnake, Oliver Eberle, Jonas Lederer +4
cs.LGcs.AIstat.MLarXiv:2006.03589v32020
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
Vardan Papyan, Yaniv Romano, Michael Elad
stat.MLcs.LGarXiv:1607.08194v42016
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig +1
cs.LGstat.MLarXiv:1901.05534v22019
On the equivalence between graph isomorphism testing and function approximation with GNNs
Zhengdao Chen, Soledad Villar, Lei Chen +1
cs.LGstat.MLarXiv:1905.12560v22019
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu
cs.LGstat.MLarXiv:1905.12843v12019
Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
Ehsan Hosseini-Asl, Robert Keynto, Ayman El-Baz
cs.LGq-bio.NCstat.MLarXiv:1607.00455v12016
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli
cs.LGstat.MLarXiv:1711.04735v12017
All-at-once Optimization for Coupled Matrix and Tensor Factorizations
Evrim Acar, Tamara G. Kolda, Daniel M. Dunlavy
math.NAphysics.data-anstat.MLarXiv:1105.3422v12011
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
Abraham J. Wyner, Matthew Olson, Justin Bleich +1
stat.MLcs.LGstat.MEarXiv:1504.07676v22015
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Lei Bai, Lina Yao, Salil. S Kanhere +2
cs.LGcs.AIstat.MLarXiv:1905.10069v12019
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
Dongkwan Kim, Alice Oh
cs.LGcs.AIcs.SIarXiv:2204.04879v12022
Variational inference for Monte Carlo objectives
Andriy Mnih, Danilo J. Rezende
cs.LGstat.MLarXiv:1602.06725v22016
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui +1
cs.LGcs.NEstat.MLarXiv:1812.02637v42018
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
Juan Miguel Lopez Alcaraz, Nils Strodthoff
cs.LGstat.MLarXiv:2208.09399v32022
PixelSNAIL: An Improved Autoregressive Generative Model
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad +1
cs.LGstat.MLarXiv:1712.09763v12017
Consistency Regularization for Generative Adversarial Networks
Han Zhang, Zizhao Zhang, Augustus Odena +1
cs.LGcs.CVstat.MLarXiv:1910.12027v22019
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Meysam Sadeghi, Erik G. Larsson
cs.ITcs.CRcs.LGarXiv:1808.07713v12018
TRAK: Attributing Model Behavior at Scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas +2
stat.MLcs.LGarXiv:2303.14186v22023
A Survey of Reinforcement Learning Informed by Natural Language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar +5
cs.LGcs.AIcs.CLarXiv:1906.03926v12019
Membership Leakage in Label-Only Exposures
Zheng Li, Yang Zhang
cs.LGcs.CRstat.MLarXiv:2007.15528v32020
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound
Lin F. Yang, Mengdi Wang
cs.LGstat.MLarXiv:1905.10389v22019
Adaptively Sparse Transformers
Gonçalo M. Correia, Vlad Niculae, André F. T. Martins
cs.CLstat.MLarXiv:1909.00015v22019
Data Driven Governing Equations Approximation Using Deep Neural Networks
Tong Qin, Kailiang Wu, Dongbin Xiu
math.NAcs.LGcs.NEarXiv:1811.05537v12018
PassGAN: A Deep Learning Approach for Password Guessing
Briland Hitaj, Paolo Gasti, Giuseppe Ateniese +1
cs.CRcs.LGstat.MLarXiv:1709.00440v32017
Universal Adversarial Perturbations Against Semantic Image Segmentation
Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox +1
stat.MLcs.AIcs.CVarXiv:1704.05712v32017
Is Homophily a Necessity for Graph Neural Networks?
Yao Ma, Xiaorui Liu, Neil Shah +1
cs.LGstat.MLarXiv:2106.06134v42021
Prediction-Powered Inference
Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang +2
stat.MLcs.AIcs.LGarXiv:2301.09633v42023
Deep Network Approximation for Smooth Functions
Jianfeng Lu, Zuowei Shen, Haizhao Yang +1
cs.LGmath.NAstat.MLarXiv:2001.03040v82020
Communication Compression for Decentralized Training
Hanlin Tang, Shaoduo Gan, Ce Zhang +2
cs.LGcs.DCeess.SYarXiv:1803.06443v52018

Machine Learning (stat)

STC Antispoofing Systems for the ASVspoof2019 Challenge

Bayesian Temporal Factorization for Multidimensional Time Series Prediction

Chain of Thought Empowers Transformers to Solve Inherently Serial Problems

Action Robust Reinforcement Learning and Applications in Continuous Control

Entangled Watermarks as a Defense against Model Extraction

Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent

How Complex is your classification problem? A survey on measuring classification complexity

Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning

Bag of Tricks for Adversarial Training

Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms

Deep AutoRegressive Networks

The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation

The Kolmogorov-Arnold representation theorem revisited

Explaining the Explainer: A First Theoretical Analysis of LIME

A Bayesian Perspective on Generalization and Stochastic Gradient Descent

Neural Expectation Maximization

Learning Equations for Extrapolation and Control

Fast Differentiable Sorting and Ranking

Learned Optimizers that Scale and Generalize

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax

Mean estimation and regression under heavy-tailed distributions--a survey

Self-Imitation Learning

Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels

MineRL: A Large-Scale Dataset of Minecraft Demonstrations

Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

Auditing Black-box Models for Indirect Influence

Gradient Descent Happens in a Tiny Subspace

Deep Variational Reinforcement Learning for POMDPs

Evolution-Guided Policy Gradient in Reinforcement Learning

Higher-Order Explanations of Graph Neural Networks via Relevant Walks

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding

Lagging Inference Networks and Posterior Collapse in Variational Autoencoders

On the equivalence between graph isomorphism testing and function approximation with GNNs

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network

Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice

All-at-once Optimization for Coupled Matrix and Tensor Factorizations

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision

Variational inference for Monte Carlo objectives

MMA Training: Direct Input Space Margin Maximization through Adversarial Training

Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models

PixelSNAIL: An Improved Autoregressive Generative Model

Consistency Regularization for Generative Adversarial Networks

Adversarial Attacks on Deep-Learning Based Radio Signal Classification

TRAK: Attributing Model Behavior at Scale

A Survey of Reinforcement Learning Informed by Natural Language

Membership Leakage in Label-Only Exposures

Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound

Adaptively Sparse Transformers

Data Driven Governing Equations Approximation Using Deep Neural Networks

PassGAN: A Deep Learning Approach for Password Guessing

Universal Adversarial Perturbations Against Semantic Image Segmentation

Is Homophily a Necessity for Graph Neural Networks?

Prediction-Powered Inference

Deep Network Approximation for Smooth Functions

Communication Compression for Decentralized Training