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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5,641 to 5,700 of 6,792

Simplicial Closure and higher-order link prediction
Austin R. Benson, Rediet Abebe, Michael T. Schaub +2
cs.SIcond-mat.stat-mechmath.ATarXiv:1802.06916v22018
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey
Ammar Haydari, Yasin Yilmaz
cs.LGcs.MAeess.SParXiv:2005.00935v12020
What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang
cs.LGstat.MLarXiv:2008.11687v22020
Matrix Completion has No Spurious Local Minimum
Rong Ge, Jason D. Lee, Tengyu Ma
cs.LGcs.DSstat.MLarXiv:1605.07272v42016
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer +1
stat.MLcs.LGarXiv:1804.04368v32018
Medical image denoising using convolutional denoising autoencoders
Lovedeep Gondara
cs.CVstat.MLarXiv:1608.04667v22016
Revealing the Dark Secrets of BERT
Olga Kovaleva, Alexey Romanov, Anna Rogers +1
cs.CLcs.LGstat.MLarXiv:1908.08593v22019
Real Time Image Saliency for Black Box Classifiers
Piotr Dabkowski, Yarin Gal
stat.MLarXiv:1705.07857v12017
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir, Tong Zhang
cs.LGmath.OCstat.MLarXiv:1212.1824v22012
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt +7
cs.AIcs.LGcs.NEarXiv:1512.07679v22015
Implicit Regularization in Deep Matrix Factorization
Sanjeev Arora, Nadav Cohen, Wei Hu +1
cs.LGcs.AIcs.NEarXiv:1905.13655v32019
High-Performance Large-Scale Image Recognition Without Normalization
Andrew Brock, Soham De, Samuel L. Smith +1
cs.CVcs.LGstat.MLarXiv:2102.06171v12021
GemNet: Universal Directional Graph Neural Networks for Molecules
Johannes Gasteiger, Florian Becker, Stephan Günnemann
physics.comp-phcs.LGphysics.chem-pharXiv:2106.08903v102021
$\texttt{findr}$: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions
Victor Medina-Olivares, Stefan Lessmann, Jonathan Crook
stat.MLcs.AIcs.LGarXiv:2608.24582v12026
Avoiding Discrimination through Causal Reasoning
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo +3
stat.MLcs.CYcs.LGarXiv:1706.02744v22017
Harnessing Deep Neural Networks with Logic Rules
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu +2
cs.LGcs.AIcs.CLarXiv:1603.06318v62016
Provably adaptive sampling with uniform and remasking discrete diffusion models
Daniil Dmitriev, Zhihan Huang, Yuting Wei
cs.LGcs.ITmath.STarXiv:2608.23554v12026
Deep learning with Elastic Averaging SGD
Sixin Zhang, Anna Choromanska, Yann LeCun
cs.LGstat.MLarXiv:1412.6651v82014
Long-term Forecasting with TiDE: Time-series Dense Encoder
Abhimanyu Das, Weihao Kong, Andrew Leach +3
stat.MLcs.LGarXiv:2304.08424v52023
Dropout Training as Adaptive Regularization
Stefan Wager, Sida Wang, Percy Liang
stat.MLcs.LGstat.MEarXiv:1307.1493v22013
Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism
Orhan Firat, Kyunghyun Cho, Yoshua Bengio
cs.CLstat.MLarXiv:1601.01073v12016
On the Identifiability of the Post-Nonlinear Causal Model
Kun Zhang, Aapo Hyvarinen
stat.MLcs.LGarXiv:1205.2599v12012
Unsupervised Scalable Representation Learning for Multivariate Time Series
Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
cs.LGcs.NEstat.MLarXiv:1901.10738v42019
Why does deep and cheap learning work so well?
Henry W. Lin, Max Tegmark, David Rolnick
cond-mat.dis-nncs.LGcs.NEarXiv:1608.08225v42016
Label-Only Membership Inference Attacks
Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini +1
cs.CRcs.LGstat.MLarXiv:2007.14321v32020
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth +6
cs.LGcs.CRstat.MLarXiv:1810.12715v42018
On the Sample Complexity of the Linear Quadratic Regulator
Sarah Dean, Horia Mania, Nikolai Matni +2
math.OCcs.LGstat.MLarXiv:1710.01688v32017
Zero-Shot Learning via Semantic Similarity Embedding
Ziming Zhang, Venkatesh Saligrama
cs.CVstat.MLarXiv:1509.04767v22015
Retiring Adult: New Datasets for Fair Machine Learning
Frances Ding, Moritz Hardt, John Miller +1
cs.LGstat.MLarXiv:2108.04884v32021
A Baseline for Few-Shot Image Classification
Guneet S. Dhillon, Pratik Chaudhari, Avinash Ravichandran +1
cs.LGcs.CVstat.MLarXiv:1909.02729v52019
A Review of Cooperative Multi-Agent Deep Reinforcement Learning
Afshin OroojlooyJadid, Davood Hajinezhad
cs.LGcs.AIcs.MAarXiv:1908.03963v42019
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
Zichao Long, Yiping Lu, Bin Dong
cs.LGmath.NAphysics.comp-pharXiv:1812.04426v22018
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang +1
eess.AScs.AIcs.LGarXiv:1709.06298v22017
Real Time Speech Enhancement in the Waveform Domain
Alexandre Defossez, Gabriel Synnaeve, Yossi Adi
eess.AScs.LGcs.SDarXiv:2006.12847v32020
Data-Free Quantization Through Weight Equalization and Bias Correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort +1
cs.LGcs.CVstat.MLarXiv:1906.04721v32019
On the Relationship between Self-Attention and Convolutional Layers
Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi
cs.LGcs.CLcs.CVarXiv:1911.03584v22019
A Fourier Perspective on Model Robustness in Computer Vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens +2
cs.LGcs.CVstat.MLarXiv:1906.08988v32019
Gated Multimodal Units for Information Fusion
John Arevalo, Thamar Solorio, Manuel Montes-y-Gómez +1
stat.MLcs.LGarXiv:1702.01992v12017
Representation Learning for Dynamic Graphs: A Survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain +4
cs.LGstat.MLarXiv:1905.11485v22019
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Chun Wang, Shirui Pan, Ruiqi Hu +3
cs.LGstat.MLarXiv:1906.06532v12019
Scalable and Versatile Identification for Hierarchical Structural Causal Models: A New Look at Project STAR
Janis Aiad, Aghiles Drali, Aymen El Ouadrhiri +6
stat.MLcs.AIarXiv:2608.24500v12026
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Vitaly Feldman, Chiyuan Zhang
cs.LGstat.MLarXiv:2008.03703v12020
Stochastic Variance Reduction for Nonconvex Optimization
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra +2
math.OCcs.LGcs.NEarXiv:1603.06160v22016
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
Andy Zeng, Shuran Song, Stefan Welker +3
cs.ROcs.AIcs.CVarXiv:1803.09956v32018
Large-Scale Learnable Graph Convolutional Networks
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
cs.LGstat.MLarXiv:1808.03965v12018
Fast AutoAugment
Sungbin Lim, Ildoo Kim, Taesup Kim +2
cs.LGcs.CVstat.MLarXiv:1905.00397v22019
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
Lei Chen, Le Wu, Richang Hong +2
cs.IRcs.LGstat.MLarXiv:2001.10167v12020
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chételat, Nicola Ferroni +2
cs.LGmath.OCstat.MLarXiv:1906.01629v32019
The Natural Language Decathlon: Multitask Learning as Question Answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong +1
cs.CLcs.AIcs.LGarXiv:1806.08730v12018
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Harini Suresh, John V. Guttag
cs.LGstat.MLarXiv:1901.10002v52019
Learning Confidence for Out-of-Distribution Detection in Neural Networks
Terrance DeVries, Graham W. Taylor
stat.MLcs.LGarXiv:1802.04865v12018
Adversarial Attacks on Graph Neural Networks via Meta Learning
Daniel Zügner, Stephan Günnemann
cs.LGcs.CRstat.MLarXiv:1902.08412v22019
By-passing the Kohn-Sham equations with machine learning
Felix Brockherde, Leslie Vogt, Li Li +3
physics.comp-phcs.LGphysics.chem-pharXiv:1609.02815v32016
Fairness Without Demographics in Repeated Loss Minimization
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong +1
stat.MLcs.LGarXiv:1806.08010v22018
Learning with Pseudo-Ensembles
Philip Bachman, Ouais Alsharif, Doina Precup
stat.MLcs.LGcs.NEarXiv:1412.4864v12014
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha +5
cs.LGstat.MLarXiv:2003.12206v42020
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Jonathan Uesato, Brendan O'Donoghue, Aaron van den Oord +1
cs.LGcs.CRstat.MLarXiv:1802.05666v22018
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang +4
cs.LGcs.IRstat.MLarXiv:1905.04413v32019
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Juntang Zhuang, Tommy Tang, Yifan Ding +4
cs.LGcs.CVstat.MLarXiv:2010.07468v52020
A Variational Perspective on Accelerated Methods in Optimization
Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
math.OCcs.LGstat.MLarXiv:1603.04245v12016

Machine Learning (stat)

Simplicial Closure and higher-order link prediction

Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

What is being transferred in transfer learning?

Matrix Completion has No Spurious Local Minimum

Regularisation of Neural Networks by Enforcing Lipschitz Continuity

Medical image denoising using convolutional denoising autoencoders

Revealing the Dark Secrets of BERT

Real Time Image Saliency for Black Box Classifiers

Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes

Deep Reinforcement Learning in Large Discrete Action Spaces

Implicit Regularization in Deep Matrix Factorization

High-Performance Large-Scale Image Recognition Without Normalization

GemNet: Universal Directional Graph Neural Networks for Molecules

$\texttt{findr}$: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions

Avoiding Discrimination through Causal Reasoning

Harnessing Deep Neural Networks with Logic Rules

Provably adaptive sampling with uniform and remasking discrete diffusion models

Deep learning with Elastic Averaging SGD

Long-term Forecasting with TiDE: Time-series Dense Encoder

Dropout Training as Adaptive Regularization

Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism

On the Identifiability of the Post-Nonlinear Causal Model

Unsupervised Scalable Representation Learning for Multivariate Time Series

Why does deep and cheap learning work so well?

Label-Only Membership Inference Attacks

On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

On the Sample Complexity of the Linear Quadratic Regulator

Zero-Shot Learning via Semantic Similarity Embedding

Retiring Adult: New Datasets for Fair Machine Learning

A Baseline for Few-Shot Image Classification

A Review of Cooperative Multi-Agent Deep Reinforcement Learning

PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

Real Time Speech Enhancement in the Waveform Domain

Data-Free Quantization Through Weight Equalization and Bias Correction

On the Relationship between Self-Attention and Convolutional Layers

A Fourier Perspective on Model Robustness in Computer Vision

Gated Multimodal Units for Information Fusion

Representation Learning for Dynamic Graphs: A Survey

Attributed Graph Clustering: A Deep Attentional Embedding Approach

Scalable and Versatile Identification for Hierarchical Structural Causal Models: A New Look at Project STAR

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

Stochastic Variance Reduction for Nonconvex Optimization

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

Large-Scale Learnable Graph Convolutional Networks

Fast AutoAugment

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

The Natural Language Decathlon: Multitask Learning as Question Answering

A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

Learning Confidence for Out-of-Distribution Detection in Neural Networks

Adversarial Attacks on Graph Neural Networks via Meta Learning

By-passing the Kohn-Sham equations with machine learning

Fairness Without Demographics in Repeated Loss Minimization

Learning with Pseudo-Ensembles

Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

Adversarial Risk and the Dangers of Evaluating Against Weak Attacks

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

A Variational Perspective on Accelerated Methods in Optimization