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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2,101 to 2,160 of 6,792

Energy Efficient Federated Learning Over Wireless Communication Networks
Zhaohui Yang, Mingzhe Chen, Walid Saad +2
cs.ITcs.LGstat.MLarXiv:1911.02417v22019
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yuang Jiang, Shiqiang Wang, Victor Valls +4
cs.LGcs.DCstat.MLarXiv:1909.12326v52019
Distributionally Robust Optimization: A Review
Hamed Rahimian, Sanjay Mehrotra
math.OCcs.LGstat.MLarXiv:1908.05659v12019
On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein +2
cs.LGstat.MLarXiv:1907.13625v22019
Multi-task Self-Supervised Learning for Human Activity Detection
Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien
cs.LGstat.MLarXiv:1907.11879v12019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu, Jian Li
cs.LGcs.NEstat.MLarXiv:1906.05890v42019
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
Nicholas Geneva, Nicholas Zabaras
physics.comp-phcs.LGstat.MLarXiv:1906.05747v32019
Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack +19
cs.CYcs.AIcs.LGarXiv:1906.05433v22019
Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning
Zhouchen Lin, Risheng Liu, Huan Li
math.NAcs.LGmath.OCarXiv:1310.5035v22013
An Improved Analysis of Training Over-parameterized Deep Neural Networks
Difan Zou, Quanquan Gu
cs.LGmath.OCstat.MLarXiv:1906.04688v12019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Hadi Salman, Greg Yang, Jerry Li +4
cs.LGcs.CRstat.MLarXiv:1906.04584v52019
Cormorant: Covariant Molecular Neural Networks
Brandon Anderson, Truong-Son Hy, Risi Kondor
physics.comp-phcs.LGstat.MLarXiv:1906.04015v32019
An Introduction to Variational Autoencoders
Diederik P. Kingma, Max Welling
cs.LGstat.MLarXiv:1906.02691v32019
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
stat.MLcs.LGphysics.chem-pharXiv:1906.00957v32019
Comparison of non-linear activation functions for deep neural networks on MNIST classification task
Dabal Pedamonti
cs.LGstat.MLarXiv:1804.02763v12018
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
Yuan Cao, Quanquan Gu
cs.LGmath.OCstat.MLarXiv:1905.13210v32019
Improved Precision and Recall Metric for Assessing Generative Models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine +2
stat.MLcs.LGcs.NEarXiv:1904.06991v32019
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
Chao Ning, Fengqi You
cs.LGmath.OCstat.MLarXiv:1904.01934v12019
Customer churn prediction in telecom using machine learning and social network analysis in big data platform
Abdelrahim Kasem Ahmad, Assef Jafar, Kadan Aljoumaa
cs.CYcs.DCcs.LGarXiv:1904.00690v12019
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin +8
cs.LGstat.MLarXiv:1903.03096v42019
A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
Amy Zhang, Nicolas Ballas, Joelle Pineau
cs.LGcs.AIstat.MLarXiv:1806.07937v22018
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning
Meixin Zhu, Xuesong Wang, Yinhai Wang
cs.LGcs.AIstat.MLarXiv:1901.00569v12019
Federated Learning via Over-the-Air Computation
Kai Yang, Tao Jiang, Yuanming Shi +1
cs.LGcs.ITeess.SParXiv:1812.11750v32018
An Introduction to Deep Reinforcement Learning
Vincent Francois-Lavet, Peter Henderson, Riashat Islam +2
cs.LGcs.AIstat.MLarXiv:1811.12560v22018
Learning Models with Uniform Performance via Distributionally Robust Optimization
John Duchi, Hongseok Namkoong
stat.MLcs.LGarXiv:1810.08750v62018
Automated software vulnerability detection with machine learning
Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell +13
cs.SEcs.LGstat.MLarXiv:1803.04497v22018
MixUp as Locally Linear Out-Of-Manifold Regularization
Hongyu Guo, Yongyi Mao, Richong Zhang
cs.LGcs.AIstat.MLarXiv:1809.02499v32018
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
Zonghao Huang, Rui Hu, Yuanxiong Guo +2
cs.LGstat.MLarXiv:1808.10101v62018
Exploring Connections Between Active Learning and Model Extraction
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli +2
cs.LGcs.CRstat.MLarXiv:1811.02054v62018
Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
Tomek Korbak, Mikita Balesni, Elizabeth Barnes +38
cs.AIcs.LGstat.MLarXiv:2507.11473v22025
Towards Optimal Power Control via Ensembling Deep Neural Networks
Fei Liang, Cong Shen, Wei Yu +1
eess.SPcs.ITstat.MLarXiv:1807.10025v22018
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
cs.LGmath.OCstat.MLarXiv:1011.0097v12010
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, Clément Hongler
cs.LGcs.NEmath.PRarXiv:1806.07572v42018
Continuous Learning in Single-Incremental-Task Scenarios
Davide Maltoni, Vincenzo Lomonaco
cs.LGcs.AIcs.CVarXiv:1806.08568v32018
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani +1
cs.LGcs.AIcs.CYarXiv:1806.06055v32018
Scaling provable adversarial defenses
Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen +1
cs.LGcs.AImath.OCarXiv:1805.12514v22018
Estimating the intrinsic dimension of datasets by a minimal neighborhood information
Elena Facco, Maria d'Errico, Alex Rodriguez +1
stat.MLcs.LGarXiv:1803.06992v12018
Predictive Uncertainty Estimation via Prior Networks
Andrey Malinin, Mark Gales
stat.MLcs.LGarXiv:1802.10501v42018
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha +1
cs.CVcs.LGstat.AParXiv:1802.08717v12018
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification
Max Simchowitz, Horia Mania, Stephen Tu +2
cs.LGmath.OCstat.MLarXiv:1802.08334v42018
Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu +2
stat.MLcs.AIcs.CRarXiv:1802.03471v42018
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin +5
cs.LGstat.MLarXiv:1801.07756v52018
Adversarial Examples: Attacks and Defenses for Deep Learning
Xiaoyong Yuan, Pan He, Qile Zhu +1
cs.LGcs.CRcs.CVarXiv:1712.07107v32017
Nonparametric regression using deep neural networks with ReLU activation function
Johannes Schmidt-Hieber
math.STcs.LGstat.MLarXiv:1708.06633v52017
A Brief Survey of Deep Reinforcement Learning
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage +1
cs.LGcs.AIcs.CVarXiv:1708.05866v22017
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Pan Xu, Jinghui Chen, Difan Zou +1
stat.MLcs.LGmath.OCarXiv:1707.06618v32017
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
Weinan E, Jiequn Han, Arnulf Jentzen
math.NAcs.LGcs.NEarXiv:1706.04702v12017
DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization
Amir Ali Ahmadi, Anirudha Majumdar
math.OCcs.DSeess.SYarXiv:1706.02586v32017
Structured sparsity-inducing norms through submodular functions
Francis Bach
cs.LGmath.OCstat.MLarXiv:1008.4220v32010
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Zhi Li, Wei Shi, Ming Yan
math.OCcs.DCcs.LGarXiv:1704.07807v22017
Deep Reinforcement Learning framework for Autonomous Driving
Ahmad El Sallab, Mohammed Abdou, Etienne Perot +1
stat.MLcs.LGcs.ROarXiv:1704.02532v12017
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
Gonzalo Mena, Jonathan Weed
math.STcs.LGstat.MLarXiv:1905.11882v22019
Neural Execution of Graph Algorithms
Petar Veličković, Rex Ying, Matilde Padovano +2
stat.MLcs.AIcs.DSarXiv:1910.10593v22019
Membership Inference Attacks against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song +1
cs.CRcs.LGstat.MLarXiv:1610.05820v22016
Safe and Efficient Off-Policy Reinforcement Learning
Rémi Munos, Tom Stepleton, Anna Harutyunyan +1
cs.LGcs.AIstat.MLarXiv:1606.02647v22016
The CMA Evolution Strategy: A Tutorial
Nikolaus Hansen
cs.LGstat.MLarXiv:1604.00772v22016
A Geometric Analysis of Phase Retrieval
Ju Sun, Qing Qu, John Wright
cs.ITmath.OCstat.MLarXiv:1602.06664v32016
Generative Modelling With Inverse Heat Dissipation
Severi Rissanen, Markus Heinonen, Arno Solin
cs.CVcs.LGstat.MLarXiv:2206.13397v72022
Model-Based Active Exploration
Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez
cs.LGcs.AIcs.ITarXiv:1810.12162v52018
How much does your data exploration overfit? Controlling bias via information usage
Daniel Russo, James Zou
stat.MLcs.LGarXiv:1511.05219v32015

Machine Learning (stat)

Energy Efficient Federated Learning Over Wireless Communication Networks

Model Pruning Enables Efficient Federated Learning on Edge Devices

Distributionally Robust Optimization: A Review

On Mutual Information Maximization for Representation Learning

Multi-task Self-Supervised Learning for Human Activity Detection

Gradient Descent Maximizes the Margin of Homogeneous Neural Networks

Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks

Tackling Climate Change with Machine Learning

Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning

An Improved Analysis of Training Over-parameterized Deep Neural Networks

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Cormorant: Covariant Molecular Neural Networks

An Introduction to Variational Autoencoders

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

Comparison of non-linear activation functions for deep neural networks on MNIST classification task

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

Improved Precision and Recall Metric for Assessing Generative Models

Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming

Customer churn prediction in telecom using machine learning and social network analysis in big data platform

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning

Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning

Federated Learning via Over-the-Air Computation

An Introduction to Deep Reinforcement Learning

Learning Models with Uniform Performance via Distributionally Robust Optimization

Automated software vulnerability detection with machine learning

MixUp as Locally Linear Out-Of-Manifold Regularization

DP-ADMM: ADMM-based Distributed Learning with Differential Privacy

Exploring Connections Between Active Learning and Model Extraction

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety

Towards Optimal Power Control via Ensembling Deep Neural Networks

Sparse Inverse Covariance Selection via Alternating Linearization Methods

Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Continuous Learning in Single-Incremental-Task Scenarios

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Scaling provable adversarial defenses

Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Predictive Uncertainty Estimation via Prior Networks

Deep learning in radiology: an overview of the concepts and a survey of the state of the art

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification

Certified Robustness to Adversarial Examples with Differential Privacy

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Adversarial Examples: Attacks and Defenses for Deep Learning

Nonparametric regression using deep neural networks with ReLU activation function

A Brief Survey of Deep Reinforcement Learning

Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization

Structured sparsity-inducing norms through submodular functions

A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates

Deep Reinforcement Learning framework for Autonomous Driving

Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem

Neural Execution of Graph Algorithms

Membership Inference Attacks against Machine Learning Models

Safe and Efficient Off-Policy Reinforcement Learning

The CMA Evolution Strategy: A Tutorial

A Geometric Analysis of Phase Retrieval

Generative Modelling With Inverse Heat Dissipation

Model-Based Active Exploration

How much does your data exploration overfit? Controlling bias via information usage