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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1,201 to 1,260 of 6,790

The Generalized Reparameterization Gradient
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
stat.MLarXiv:1610.02287v32016
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Yuan Yin, Vincent Le Guen, Jérémie Dona +4
stat.MLcs.AIcs.CVarXiv:2010.04456v62020
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang +1
cs.LGcs.AIstat.MLarXiv:1906.02174v32019
Impact of regularization on Spectral Clustering
Antony Joseph, Bin Yu
stat.MLarXiv:1312.1733v22013
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
stat.MLcs.LGarXiv:1810.11646v12018
What are the Statistical Limits of Offline RL with Linear Function Approximation?
Ruosong Wang, Dean P. Foster, Sham M. Kakade
cs.LGcs.AImath.OCarXiv:2010.11895v12020
Provable Self-Play Algorithms for Competitive Reinforcement Learning
Yu Bai, Chi Jin
cs.LGcs.AIstat.MLarXiv:2002.04017v32020
Deep Models of Interactions Across Sets
Jason Hartford, Devon R Graham, Kevin Leyton-Brown +1
stat.MLcs.LGarXiv:1803.02879v22018
Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
Charles H. Martin, Tongsu, Peng +1
cs.LGphysics.data-anstat.MLarXiv:2002.06716v22020
Scale-Equivariant Steerable Networks
Ivan Sosnovik, Michał Szmaja, Arnold Smeulders
cs.CVcs.LGstat.MLarXiv:1910.11093v22019
Dataset Inference: Ownership Resolution in Machine Learning
Pratyush Maini, Mohammad Yaghini, Nicolas Papernot
stat.MLcs.CRcs.LGarXiv:2104.10706v12021
Locally Private Graph Neural Networks
Sina Sajadmanesh, Daniel Gatica-Perez
cs.LGcs.CRstat.MLarXiv:2006.05535v92020
A Gang of Bandits
Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella
cs.LGcs.SIstat.MLarXiv:1306.0811v32013
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Patrick Schwab, Lorenz Linhardt, Stefan Bauer +2
cs.LGstat.MLarXiv:1902.00981v32019
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
Zhize Li, Hongyan Bao, Xiangliang Zhang +1
cs.LGcs.AIcs.DSarXiv:2008.10898v32020
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent
Yunwen Lei, Yiming Ying
cs.LGstat.MLarXiv:2006.08157v12020
A Novel Community Detection Based Genetic Algorithm for Feature Selection
Mehrdad Rostami, Kamal Berahmand, Saman Forouzandeh
cs.LGcs.NEstat.MLarXiv:2008.03543v12020
Group-Invariant Quantum Machine Learning
Martin Larocca, Frederic Sauvage, Faris M. Sbahi +3
quant-phcs.LGstat.MLarXiv:2205.02261v22022
An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification
Abien Fred Agarap
cs.CVcs.LGcs.NEarXiv:1712.03541v22017
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
Heng Chang, Yu Rong, Tingyang Xu +5
cs.SIcs.CRcs.LGarXiv:1908.01297v52019
LLMs Will Always Hallucinate, and We Need to Live With This
Sourav Banerjee, Ayushi Agarwal, Saloni Singla
stat.MLcs.LGarXiv:2409.05746v12024
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
Cathy Wu, Aravind Rajeswaran, Yan Duan +5
cs.LGcs.AIstat.MLarXiv:1803.07246v12018
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang
stat.MLcs.LGarXiv:1705.01936v32017
Boundary-Seeking Generative Adversarial Networks
R Devon Hjelm, Athul Paul Jacob, Tong Che +3
stat.MLcs.LGarXiv:1702.08431v42017
RMDL: Random Multimodel Deep Learning for Classification
Kamran Kowsari, Mojtaba Heidarysafa, Donald E. Brown +2
cs.LGcs.AIcs.CVarXiv:1805.01890v22018
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Jannik Kossen, Neil Band, Clare Lyle +3
cs.LGstat.MLarXiv:2106.02584v22021
Interpretation of Natural Language Rules in Conversational Machine Reading
Marzieh Saeidi, Max Bartolo, Patrick Lewis +5
cs.CLcs.LGstat.MLarXiv:1809.01494v12018
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Nils Strodthoff, Claas Strodthoff
cs.CYcs.LGstat.MLarXiv:1806.07385v22018
Diverse mini-batch Active Learning
Fedor Zhdanov
cs.LGstat.MLarXiv:1901.05954v12019
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Anees Kazi, Shayan shekarforoush, S. Arvind krishna +6
cs.LGstat.MLarXiv:1903.04233v12019
Few-Shot Learning with Localization in Realistic Settings
Davis Wertheimer, Bharath Hariharan
cs.CVcs.AIcs.LGarXiv:1904.08502v22019
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Muhammad Shaban, Ruqayya Awan, Muhammad Moazam Fraz +3
eess.IVcs.LGstat.MLarXiv:1907.09478v12019
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis +1
cs.LGstat.MLarXiv:1912.02400v22019
If Influence Functions are the Answer, Then What is the Question?
Juhan Bae, Nathan Ng, Alston Lo +2
cs.LGstat.MLarXiv:2209.05364v12022
The Robust Manifold Defense: Adversarial Training using Generative Models
Ajil Jalal, Andrew Ilyas, Constantinos Daskalakis +1
cs.CVcs.CRcs.LGarXiv:1712.09196v52017
Unsupervised Anomaly Localization using Variational Auto-Encoders
David Zimmerer, Fabian Isensee, Jens Petersen +2
cs.LGeess.IVstat.MLarXiv:1907.02796v22019
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu +2
eess.SPcs.LGcs.NIarXiv:2005.05321v32020
Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference
David Yallup, Namu Kroupa, Will Handley
stat.COcs.LGstat.MLarXiv:2601.23252v22026
Inductive Biases and Variable Creation in Self-Attention Mechanisms
Benjamin L. Edelman, Surbhi Goel, Sham Kakade +1
cs.LGstat.MLarXiv:2110.10090v22021
Training verified learners with learned verifiers
Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth +4
cs.LGstat.MLarXiv:1805.10265v22018
Online 3D Bin Packing with Constrained Deep Reinforcement Learning
Hang Zhao, Qijin She, Chenyang Zhu +2
cs.LGstat.MLarXiv:2006.14978v52020
Towards minimax policies for online linear optimization with bandit feedback
Sébastien Bubeck, Nicolò Cesa-Bianchi, Sham M. Kakade
cs.LGstat.MLarXiv:1202.3079v12012
A Signal Propagation Perspective for Pruning Neural Networks at Initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould +1
cs.LGcs.CVstat.MLarXiv:1906.06307v22019
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
Wenqi Wei, Ling Liu, Margaret Loper +4
cs.LGcs.CRstat.MLarXiv:2004.10397v22020
Black box variational inference for state space models
Evan Archer, Il Memming Park, Lars Buesing +2
stat.MLarXiv:1511.07367v12015
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian V Dalca, Luca Saglietti +2
cs.LGstat.MLarXiv:1810.11738v22018
Privately Learning High-Dimensional Distributions
Gautam Kamath, Jerry Li, Vikrant Singhal +1
cs.DScs.CRcs.LGarXiv:1805.00216v32018
Tight Analyses for Non-Smooth Stochastic Gradient Descent
Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan +1
cs.LGmath.OCstat.MLarXiv:1812.05217v12018
Understanding Neural Networks via Feature Visualization: A survey
Anh Nguyen, Jason Yosinski, Jeff Clune
cs.LGcs.AIcs.CVarXiv:1904.08939v12019
Escaping Saddles with Stochastic Gradients
Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi +1
cs.LGmath.OCstat.MLarXiv:1803.05999v22018
PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoders
Chloé Hashimoto-Cullen, Ghislain Agoua, Benjamin Guedj +1
stat.MLcs.LGarXiv:2609.05212v12026
On the origin of neural scaling laws: from random graphs to natural language
Maissam Barkeshli, Alberto Alfarano, Andrey Gromov
cs.LGcond-mat.dis-nncs.AIarXiv:2601.10684v12026
Confounding-Robust Policy Improvement
Nathan Kallus, Angela Zhou
cs.LGstat.MLarXiv:1805.08593v32018
Minimax Lower Bound for Estimating Diffusion-based Local Intrinsic Dimension
Jaehee Seo, Wontae Jeong, Jisu Kim
stat.MLcs.LGmath.STarXiv:2609.04822v12026
FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search
Cassandra Durr, Alvaro Köhn-Luque, Chris Jewell +1
stat.MLcs.LGphysics.data-anarXiv:2609.05207v12026
An Alternative Probabilistic Interpretation of the Huber Loss
Gregory P. Meyer
stat.MLcs.CVcs.LGarXiv:1911.02088v32019
An Analysis of Self-supervised Pre-training with Dependent Samples
Maximilian Fleissner, Debarghya Ghoshdastidar, Samory Kpotufe
stat.MLcs.LGarXiv:2609.05031v12026
Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks
Zhenguo Nie, Haoliang Jiang, Levent Burak Kara
cs.LGstat.MLarXiv:1808.08914v32018
Knowledge distillation from multi-modal to mono-modal segmentation networks
Minhao Hu, Matthis Maillard, Ya Zhang +4
cs.CVcs.AIstat.MLarXiv:2106.09564v12021
An improvement of the convergence proof of the ADAM-Optimizer
Sebastian Bock, Josef Goppold, Martin Weiß
cs.LGcs.AIstat.MLarXiv:1804.10587v12018

Machine Learning (stat)

The Generalized Reparameterization Gradient

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

Impact of regularization on Spectral Clustering

Removing Hidden Confounding by Experimental Grounding

What are the Statistical Limits of Offline RL with Linear Function Approximation?

Provable Self-Play Algorithms for Competitive Reinforcement Learning

Deep Models of Interactions Across Sets

Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

Scale-Equivariant Steerable Networks

Dataset Inference: Ownership Resolution in Machine Learning

Locally Private Graph Neural Networks

A Gang of Bandits

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization

Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent

A Novel Community Detection Based Genetic Algorithm for Feature Selection

Group-Invariant Quantum Machine Learning

An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification

A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models

LLMs Will Always Hallucinate, and We Need to Live With This

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

Boundary-Seeking Generative Adversarial Networks

RMDL: Random Multimodel Deep Learning for Classification

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning

Interpretation of Natural Language Rules in Conversational Machine Reading

Detecting and interpreting myocardial infarction using fully convolutional neural networks

Diverse mini-batch Active Learning

InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Few-Shot Learning with Localization in Realistic Settings

Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images

Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space

If Influence Functions are the Answer, Then What is the Question?

The Robust Manifold Defense: Adversarial Training using Generative Models

Unsupervised Anomaly Localization using Variational Auto-Encoders

Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers

Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference

Inductive Biases and Variable Creation in Self-Attention Mechanisms

Training verified learners with learned verifiers

Online 3D Bin Packing with Constrained Deep Reinforcement Learning

Towards minimax policies for online linear optimization with bandit feedback

A Signal Propagation Perspective for Pruning Neural Networks at Initialization

A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

Black box variational inference for state space models

Gaussian Process Prior Variational Autoencoders

Privately Learning High-Dimensional Distributions

Tight Analyses for Non-Smooth Stochastic Gradient Descent

Understanding Neural Networks via Feature Visualization: A survey

Escaping Saddles with Stochastic Gradients

PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoders

On the origin of neural scaling laws: from random graphs to natural language

Confounding-Robust Policy Improvement

Minimax Lower Bound for Estimating Diffusion-based Local Intrinsic Dimension

FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search

An Alternative Probabilistic Interpretation of the Huber Loss

An Analysis of Self-supervised Pre-training with Dependent Samples

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

Knowledge distillation from multi-modal to mono-modal segmentation networks

An improvement of the convergence proof of the ADAM-Optimizer