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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5,701 to 5,760 of 6,792

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu +4
cs.LGcs.ROstat.MLarXiv:1803.11347v62018
InterpretML: A Unified Framework for Machine Learning Interpretability
Harsha Nori, Samuel Jenkins, Paul Koch +1
cs.LGstat.MLarXiv:1909.09223v12019
Three Approaches for Personalization with Applications to Federated Learning
Yishay Mansour, Mehryar Mohri, Jae Ro +1
cs.LGstat.MLarXiv:2002.10619v22020
Adversarial Logit Pairing
Harini Kannan, Alexey Kurakin, Ian Goodfellow
cs.LGstat.MLarXiv:1803.06373v12018
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
Arka Daw, Anuj Karpatne, William Watkins +2
cs.LGcs.AIcs.CVarXiv:1710.11431v32017
The FLUXCOM ensemble of global land-atmosphere energy fluxes
Martin Jung, Sujan Koirala, Ulrich Weber +7
physics.ao-phcs.LGstat.MLarXiv:1812.04951v12018
The Linear Representation Hypothesis and the Geometry of Large Language Models
Kiho Park, Yo Joong Choe, Victor Veitch
cs.CLcs.AIcs.LGarXiv:2311.03658v22023
Optimization of Molecules via Deep Reinforcement Learning
Zhenpeng Zhou, Steven Kearnes, Li Li +2
cs.LGcs.AIstat.MLarXiv:1810.08678v32018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen, Le Song, Martin J. Wainwright +1
cs.LGcs.AIstat.MLarXiv:1802.07814v22018
Time-Series Anomaly Detection Service at Microsoft
Hansheng Ren, Bixiong Xu, Yujing Wang +7
cs.LGstat.MLarXiv:1906.03821v12019
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
Divyansh Kaushik, Eduard Hovy, Zachary C. Lipton
cs.CLcs.AIcs.LGarXiv:1909.12434v22019
Generalized Correntropy for Robust Adaptive Filtering
Badong Chen, Lei Xing, Haiquan Zhao +2
stat.MLcs.ITarXiv:1504.02931v12015
Deep Learning Techniques for Inverse Problems in Imaging
Gregory Ongie, Ajil Jalal, Christopher A. Metzler +3
eess.IVcs.LGstat.MLarXiv:2005.06001v12020
A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman +1
cs.LGcs.AIstat.MLarXiv:1805.07708v12018
Method, Mind, and Morality: How People Make Sense of Artificial Intelligence
Jacy Reese Anthis, Erik Brynjolfsson, James Evans
cs.CYcs.AIcs.CLarXiv:2608.24748v12026
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen +3
cs.LGcs.CVstat.MLarXiv:1912.03263v32019
A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning
Soham Chatterjee, Rwitobroto Dey, Smarajit Bose
stat.MLcs.LGarXiv:2608.24195v12026
Few-Shot Unsupervised Image-to-Image Translation
Ming-Yu Liu, Xun Huang, Arun Mallya +4
cs.CVcs.AIcs.GRarXiv:1905.01723v22019
Concrete Dropout
Yarin Gal, Jiri Hron, Alex Kendall
stat.MLarXiv:1705.07832v12017
Deep Anomaly Detection Using Geometric Transformations
Izhak Golan, Ran El-Yaniv
cs.LGstat.MLarXiv:1805.10917v22018
An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks
Brian Kenji Iwana, Seiichi Uchida
cs.LGstat.MLarXiv:2007.15951v42020
Learning from Protein Structure with Geometric Vector Perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana +2
q-bio.BMcs.LGstat.MLarXiv:2009.01411v32020
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks
Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers +5
cs.CVcs.LGstat.MLarXiv:1807.10225v22018
Neural Variational Inference for Text Processing
Yishu Miao, Lei Yu, Phil Blunsom
cs.CLcs.LGstat.MLarXiv:1511.06038v42015
Mitigating Sybils in Federated Learning Poisoning
Clement Fung, Chris J. M. Yoon, Ivan Beschastnikh
cs.LGcs.CRcs.DCarXiv:1808.04866v52018
Learning with a Wasserstein Loss
Charlie Frogner, Chiyuan Zhang, Hossein Mobahi +2
cs.LGcs.CVstat.MLarXiv:1506.05439v32015
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
Werner Zellinger, Thomas Grubinger, Edwin Lughofer +2
stat.MLcs.LGarXiv:1702.08811v32017
Physics-informed learning of governing equations from scarce data
Zhao Chen, Yang Liu, Hao Sun
cs.LGphysics.comp-phphysics.data-anarXiv:2005.03448v32020
Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg +4
cs.LGcs.AIstat.MLarXiv:1806.01242v12018
On Causal and Anticausal Learning
Bernhard Schoelkopf, Dominik Janzing, Jonas Peters +3
cs.LGstat.MLarXiv:1206.6471v12012
A Simple Convolutional Generative Network for Next Item Recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis +2
cs.IRcs.LGstat.MLarXiv:1808.05163v42018
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Oscar Li, Hao Liu, Chaofan Chen +1
cs.AIcs.LGstat.MLarXiv:1710.04806v22017
Strong rules for discarding predictors in lasso-type problems
Robert Tibshirani, Jacob Bien, Jerome Friedman +4
math.STstat.MLarXiv:1011.2234v22010
Challenges of Real-World Reinforcement Learning
Gabriel Dulac-Arnold, Daniel Mankowitz, Todd Hester
cs.LGcs.AIcs.ROarXiv:1904.12901v12019
Joint Distribution Optimal Transportation for Domain Adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard +1
stat.MLcs.LGarXiv:1705.08848v22017
Gaussian Process Kernels for Pattern Discovery and Extrapolation
Andrew Gordon Wilson, Ryan Prescott Adams
stat.MLcs.AIstat.MEarXiv:1302.4245v32013
Causal Discovery with Continuous Additive Noise Models
Jonas Peters, Joris Mooij, Dominik Janzing +1
stat.MLarXiv:1309.6779v42013
Variational Adversarial Active Learning
Samarth Sinha, Sayna Ebrahimi, Trevor Darrell
cs.LGcs.CVstat.MLarXiv:1904.00370v32019
The Variational Fair Autoencoder
Christos Louizos, Kevin Swersky, Yujia Li +2
stat.MLcs.LGarXiv:1511.00830v62015
Detecting and Correcting for Label Shift with Black Box Predictors
Zachary C. Lipton, Yu-Xiang Wang, Alex Smola
cs.LGcs.AIcs.NEarXiv:1802.03916v32018
On the Robustness of Interpretability Methods
David Alvarez-Melis, Tommi S. Jaakkola
cs.LGstat.MLarXiv:1806.08049v12018
Understanding Deep Convolutional Networks
Stéphane Mallat
stat.MLcs.CVcs.LGarXiv:1601.04920v12016
Norm-Based Capacity Control in Neural Networks
Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
cs.LGcs.AIcs.NEarXiv:1503.00036v22015
Micro-Diffusion Compression - Binary Tree Tweedie Denoising for Online Probability Estimation
Roberto Tacconelli
stat.MLcs.ITcs.LGarXiv:2603.08771v32026
A unified deep artificial neural network approach to partial differential equations in complex geometries
Jens Berg, Kaj Nyström
stat.MLcs.LGarXiv:1711.06464v22017
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa +5
stat.MLarXiv:1101.2489v32011
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Yihan Jiang, Jakub Konečný, Keith Rush +1
cs.LGstat.MLarXiv:1909.12488v22019
Network Representation Learning: A Survey
Daokun Zhang, Jie Yin, Xingquan Zhu +1
cs.SIcs.LGstat.MLarXiv:1801.05852v32017
Parameter Space Noise for Exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal +6
cs.LGcs.AIcs.NEarXiv:1706.01905v22017
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Pin-Yu Chen, Yash Sharma, Huan Zhang +2
stat.MLcs.CRcs.LGarXiv:1709.04114v32017
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu +1
cs.LGstat.MLarXiv:1910.00189v22019
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
Xu Han, Hao Zhu, Pengfei Yu +4
cs.LGcs.AIcs.CLarXiv:1810.10147v22018
Generalisation in humans and deep neural networks
Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber +3
cs.CVcs.AIcs.LGarXiv:1808.08750v32018
Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithra Raghu +2
stat.MLcond-mat.dis-nncs.LGarXiv:1606.05340v22016
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Kaiqing Zhang, Zhuoran Yang, Han Liu +2
cs.LGcs.AIcs.MAarXiv:1802.08757v22018
Hyper-Parameter Optimization: A Review of Algorithms and Applications
Tong Yu, Hong Zhu
cs.LGstat.MLarXiv:2003.05689v12020
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang +2
cs.LGcs.SIstat.MLarXiv:2006.15437v12020
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li, Chenjie Gu, Thomas Dullien +2
cs.LGstat.MLarXiv:1904.12787v22019
Value Iteration Networks
Aviv Tamar, Yi Wu, Garrett Thomas +2
cs.AIcs.LGcs.NEarXiv:1602.02867v42016
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
Maziar Raissi, Paris Perdikaris, George Em Karniadakis
cs.AIcs.LGmath.AParXiv:1711.10566v12017

Machine Learning (stat)

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

InterpretML: A Unified Framework for Machine Learning Interpretability

Three Approaches for Personalization with Applications to Federated Learning

Adversarial Logit Pairing

Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

The FLUXCOM ensemble of global land-atmosphere energy fluxes

The Linear Representation Hypothesis and the Geometry of Large Language Models

Optimization of Molecules via Deep Reinforcement Learning

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

Time-Series Anomaly Detection Service at Microsoft

Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

Generalized Correntropy for Robust Adaptive Filtering

Deep Learning Techniques for Inverse Problems in Imaging

A Lyapunov-based Approach to Safe Reinforcement Learning

Method, Mind, and Morality: How People Make Sense of Artificial Intelligence

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning

Few-Shot Unsupervised Image-to-Image Translation

Concrete Dropout

Deep Anomaly Detection Using Geometric Transformations

An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks

Learning from Protein Structure with Geometric Vector Perceptrons

Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks

Neural Variational Inference for Text Processing

Mitigating Sybils in Federated Learning Poisoning

Learning with a Wasserstein Loss

Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning

Physics-informed learning of governing equations from scarce data

Graph networks as learnable physics engines for inference and control

On Causal and Anticausal Learning

A Simple Convolutional Generative Network for Next Item Recommendation

Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions

Strong rules for discarding predictors in lasso-type problems

Challenges of Real-World Reinforcement Learning

Joint Distribution Optimal Transportation for Domain Adaptation

Gaussian Process Kernels for Pattern Discovery and Extrapolation

Causal Discovery with Continuous Additive Noise Models

Variational Adversarial Active Learning

The Variational Fair Autoencoder

Detecting and Correcting for Label Shift with Black Box Predictors

On the Robustness of Interpretability Methods

Understanding Deep Convolutional Networks

Norm-Based Capacity Control in Neural Networks

Micro-Diffusion Compression - Binary Tree Tweedie Denoising for Online Probability Estimation

A unified deep artificial neural network approach to partial differential equations in complex geometries

DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model

Improving Federated Learning Personalization via Model Agnostic Meta Learning

Network Representation Learning: A Survey

Parameter Space Noise for Exploration

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

The Non-IID Data Quagmire of Decentralized Machine Learning

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

Generalisation in humans and deep neural networks

Exponential expressivity in deep neural networks through transient chaos

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

Hyper-Parameter Optimization: A Review of Algorithms and Applications

GPT-GNN: Generative Pre-Training of Graph Neural Networks

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

Value Iteration Networks

Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations