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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4,201 to 4,260 of 6,784

Model Fusion via Optimal Transport
Sidak Pal Singh, Martin Jaggi
cs.LGstat.MLarXiv:1910.05653v62019
Subgraph Matching Kernels for Attributed Graphs
Nils Kriege, Petra Mutzel
cs.LGstat.MLarXiv:1206.6483v12012
Understanding the Difficulty of Training Transformers
Liyuan Liu, Xiaodong Liu, Jianfeng Gao +2
cs.LGcs.CLstat.MLarXiv:2004.08249v32020
Show Your Work: Improved Reporting of Experimental Results
Jesse Dodge, Suchin Gururangan, Dallas Card +2
cs.LGcs.CLstat.MEarXiv:1909.03004v12019
Learning to Decompose and Disentangle Representations for Video Prediction
Jun-Ting Hsieh, Bingbin Liu, De-An Huang +2
cs.LGcs.CVstat.MLarXiv:1806.04166v22018
A unified view of entropy-regularized Markov decision processes
Gergely Neu, Anders Jonsson, Vicenç Gómez
cs.LGcs.AIstat.MLarXiv:1705.07798v12017
Stein Variational Gradient Descent as Gradient Flow
Qiang Liu
stat.MLarXiv:1704.07520v22017
Conditional Object-Centric Learning from Video
Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran +6
cs.CVcs.LGstat.MLarXiv:2111.12594v22021
Understanding the impact of entropy on policy optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi +1
cs.LGstat.MLarXiv:1811.11214v52018
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes
Biwei Huang, Kun Zhang, Jiji Zhang +4
cs.LGstat.MLarXiv:1903.01672v52019
A Kronecker-factored approximate Fisher matrix for convolution layers
Roger Grosse, James Martens
stat.MLcs.LGarXiv:1602.01407v22016
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet +1
stat.MLcs.LGarXiv:1710.05741v22017
Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-rank Matrices
T. Tony Cai, Anru Zhang
cs.ITmath.STstat.MLarXiv:1306.1154v22013
DYNOTEARS: Structure Learning from Time-Series Data
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai +4
stat.MLcs.LGarXiv:2002.00498v22020
How do Data Science Workers Collaborate? Roles, Workflows, and Tools
Amy X. Zhang, Michael Muller, Dakuo Wang
cs.HCcs.AIcs.LGarXiv:2001.06684v32020
A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
Zequn Sun, Qingheng Zhang, Wei Hu +4
cs.CLcs.AIcs.DBarXiv:2003.07743v22020
Estimating time-varying networks
Mladen Kolar, Le Song, Amr Ahmed +1
stat.MLq-bio.MNq-bio.QMarXiv:0812.5087v22008
Federated Evaluation of On-device Personalization
Kangkang Wang, Rajiv Mathews, Chloé Kiddon +3
cs.LGstat.MLarXiv:1910.10252v12019
Self-Supervised GANs via Auxiliary Rotation Loss
Ting Chen, Xiaohua Zhai, Marvin Ritter +2
cs.LGcs.CVstat.MLarXiv:1811.11212v22018
On the Convergence of Local Descent Methods in Federated Learning
Farzin Haddadpour, Mehrdad Mahdavi
cs.LGcs.DCstat.MLarXiv:1910.14425v22019
Generating Images with Sparse Representations
Charlie Nash, Jacob Menick, Sander Dieleman +1
cs.CVstat.MLarXiv:2103.03841v12021
AUC Maximization in the Era of Big Data and AI: A Survey
Tianbao Yang, Yiming Ying
cs.LGcs.AImath.OCarXiv:2203.15046v32022
Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
Dongwook Lee, Jaejun Yoo, Sungho Tak +1
cs.CVcs.AIcs.LGarXiv:1804.00432v12018
Iterative Random Forests to detect predictive and stable high-order interactions
Sumanta Basu, Karl Kumbier, James B. Brown +1
stat.MLq-bio.GNarXiv:1706.08457v42017
Energy Flow Networks: Deep Sets for Particle Jets
Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler
hep-phhep-exstat.MLarXiv:1810.05165v22018
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
Stefan Klus, Feliks Nüske, Sebastian Peitz +3
math.DSstat.MLarXiv:1909.10638v22019
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
Yuta Saito, Suguru Yaginuma, Yuta Nishino +2
stat.MLcs.IRcs.LGarXiv:1909.03601v32019
FLAML: A Fast and Lightweight AutoML Library
Chi Wang, Qingyun Wu, Markus Weimer +1
cs.LGstat.MLarXiv:1911.04706v32019
Synthesizing Tabular Data using Generative Adversarial Networks
Lei Xu, Kalyan Veeramachaneni
cs.LGstat.MLarXiv:1811.11264v12018
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
Charles H. Martin, Michael W. Mahoney
cs.LGstat.MLarXiv:1810.01075v12018
Byzantine Stochastic Gradient Descent
Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
cs.LGcs.DCcs.DSarXiv:1803.08917v12018
The Blessings of Multiple Causes
Yixin Wang, David M. Blei
stat.MLcs.LGstat.MEarXiv:1805.06826v32018
Supervised Multimodal Bitransformers for Classifying Images and Text
Douwe Kiela, Suvrat Bhooshan, Hamed Firooz +2
cs.CLcs.CVcs.LGarXiv:1909.02950v22019
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
Trapit Bansal, David Belanger, Andrew McCallum
stat.MLcs.CLcs.LGarXiv:1609.02116v22016
Sparse Canonical Correlation Analysis
David R. Hardoon, John Shawe-Taylor
stat.MLstat.MEarXiv:0908.2724v12009
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
Hongyang Yang, Xiao-Yang Liu, Shan Zhong +1
q-fin.TRq-fin.CPq-fin.PMarXiv:2511.12120v12025
Finite-Time Analysis of Kernelised Contextual Bandits
Michal Valko, Nathaniel Korda, Remi Munos +2
cs.LGstat.MLarXiv:1309.6869v12013
Contextual Markov Decision Processes
Assaf Hallak, Dotan Di Castro, Shie Mannor
stat.MLcs.LGarXiv:1502.02259v12015
Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal +4
cs.LGstat.MLarXiv:2007.06700v12020
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
Isabelle Augenstein, Christina Lioma, Dongsheng Wang +4
cs.CLcs.IRcs.LGarXiv:1909.03242v22019
Generating Sentences by Editing Prototypes
Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren +1
cs.CLcs.AIcs.LGarXiv:1709.08878v22017
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya L. Donti, Bryan Wilder +1
cs.LGcs.AIstat.MLarXiv:1905.12149v12019
Randomized Smoothing for Stochastic Optimization
John C. Duchi, Peter L. Bartlett, Martin J. Wainwright
math.OCstat.MLarXiv:1103.4296v22011
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
Yu Emma Wang, Gu-Yeon Wei, David Brooks
cs.LGcs.PFstat.MLarXiv:1907.10701v42019
Efficient Optimal Learning for Contextual Bandits
Miroslav Dudik, Daniel Hsu, Satyen Kale +4
cs.LGcs.AIstat.MLarXiv:1106.2369v12011
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev +1
cs.LGstat.MLarXiv:2102.08921v22021
Multi-Objective Counterfactual Explanations
Susanne Dandl, Christoph Molnar, Martin Binder +1
stat.MLcs.LGarXiv:2004.11165v22020
A Gentle Introduction to Deep Learning for Graphs
Davide Bacciu, Federico Errica, Alessio Micheli +1
cs.LGcs.SIstat.MLarXiv:1912.12693v22019
Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol +2
cs.LGcs.AIstat.MLarXiv:2110.02905v32021
End-to-end Deep Learning of Optical Fiber Communications
Boris Karanov, Mathieu Chagnon, Félix Thouin +5
cs.ITstat.MLarXiv:1804.04097v32018
"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models
Philippe Schwaller, Theophile Gaudin, David Lanyi +2
cs.LGstat.MLarXiv:1711.04810v22017
Learned D-AMP: Principled Neural Network based Compressive Image Recovery
Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk
stat.MLcs.LGarXiv:1704.06625v42017
Learning to Perform Physics Experiments via Deep Reinforcement Learning
Misha Denil, Pulkit Agrawal, Tejas D Kulkarni +3
stat.MLcs.AIcs.CVarXiv:1611.01843v32016
Towards Robust Evaluations of Continual Learning
Sebastian Farquhar, Yarin Gal
stat.MLcs.LGarXiv:1805.09733v32018
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference
Klas Leino, Matt Fredrikson
cs.LGcs.CRstat.MLarXiv:1906.11798v22019
Pre-training Tasks for Embedding-based Large-scale Retrieval
Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang +2
cs.LGcs.CLcs.IRarXiv:2002.03932v12020
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration
Cecilia S. Lee, Doug M. Baughman, Aaron Y. Lee
stat.MLcs.CVcs.LGarXiv:1612.04891v12016
Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade +1
cs.LGcs.AIstat.MLarXiv:1902.08438v42019
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
Cun Mu, Bo Huang, John Wright +1
stat.MLcs.LGarXiv:1307.5870v22013
Deep learning for smart fish farming: applications, opportunities and challenges
Xinting Yang, Song Zhang, Jintao Liu +3
cs.CVcs.LGeess.IVarXiv:2004.11848v22020

Machine Learning (stat)

Model Fusion via Optimal Transport

Subgraph Matching Kernels for Attributed Graphs

Understanding the Difficulty of Training Transformers

Show Your Work: Improved Reporting of Experimental Results

Learning to Decompose and Disentangle Representations for Video Prediction

A unified view of entropy-regularized Markov decision processes

Stein Variational Gradient Descent as Gradient Flow

Conditional Object-Centric Learning from Video

Understanding the impact of entropy on policy optimization

Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes

A Kronecker-factored approximate Fisher matrix for convolution layers

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-rank Matrices

DYNOTEARS: Structure Learning from Time-Series Data

How do Data Science Workers Collaborate? Roles, Workflows, and Tools

A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs

Estimating time-varying networks

Federated Evaluation of On-device Personalization

Self-Supervised GANs via Auxiliary Rotation Loss

On the Convergence of Local Descent Methods in Federated Learning

Generating Images with Sparse Representations

AUC Maximization in the Era of Big Data and AI: A Survey

Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks

Iterative Random Forests to detect predictive and stable high-order interactions

Energy Flow Networks: Deep Sets for Particle Jets

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control

Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback

FLAML: A Fast and Lightweight AutoML Library

Synthesizing Tabular Data using Generative Adversarial Networks

Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

Byzantine Stochastic Gradient Descent

The Blessings of Multiple Causes

Supervised Multimodal Bitransformers for Classifying Images and Text

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

Sparse Canonical Correlation Analysis

Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy

Finite-Time Analysis of Kernelised Contextual Bandits

Contextual Markov Decision Processes

Revisiting Fundamentals of Experience Replay

MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims

Generating Sentences by Editing Prototypes

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

Randomized Smoothing for Stochastic Optimization

Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Efficient Optimal Learning for Contextual Bandits

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

Multi-Objective Counterfactual Explanations

A Gentle Introduction to Deep Learning for Graphs

Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

End-to-end Deep Learning of Optical Fiber Communications

"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models

Learned D-AMP: Principled Neural Network based Compressive Image Recovery

Learning to Perform Physics Experiments via Deep Reinforcement Learning

Towards Robust Evaluations of Continual Learning

Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference

Pre-training Tasks for Embedding-based Large-scale Retrieval

Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration

Online Meta-Learning

Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

Deep learning for smart fish farming: applications, opportunities and challenges