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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6,061 to 6,120 of 6,786

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker +1
cs.LGcs.CVstat.MLarXiv:1506.07365v32015
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhuangdi Zhu, Kaixiang Lin, Anil K. Jain +1
cs.LGcs.AIstat.MLarXiv:2009.07888v72020
Unitary Evolution Recurrent Neural Networks
Martin Arjovsky, Amar Shah, Yoshua Bengio
cs.LGcs.NEstat.MLarXiv:1511.06464v42015
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris Mooij +3
stat.MLcs.LGarXiv:1705.08821v22017
Up or Down? Adaptive Rounding for Post-Training Quantization
Markus Nagel, Rana Ali Amjad, Mart van Baalen +2
cs.LGcs.CVstat.MLarXiv:2004.10568v22020
Kernel Mean Embedding of Distributions: A Review and Beyond
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur +1
stat.MLcs.LGarXiv:1605.09522v42016
Crowdsourcing Multiple Choice Science Questions
Johannes Welbl, Nelson F. Liu, Matt Gardner
cs.HCcs.AIcs.CLarXiv:1707.06209v12017
Pseudo Numerical Methods for Diffusion Models on Manifolds
Luping Liu, Yi Ren, Zhijie Lin +1
cs.CVcs.LGmath.NAarXiv:2202.09778v22022
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
Yann Dubois, Balázs Galambosi, Percy Liang +1
cs.LGcs.AIcs.CLarXiv:2404.04475v22024
Temporal Pattern Attention for Multivariate Time Series Forecasting
Shun-Yao Shih, Fan-Keng Sun, Hung-yi Lee
cs.LGcs.CLstat.MLarXiv:1809.04206v32018
Hypergraph Convolution and Hypergraph Attention
Song Bai, Feihu Zhang, Philip H. S. Torr
cs.LGcs.CVstat.MLarXiv:1901.08150v22019
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Medhat Gaber
eess.IVcs.CVcs.LGarXiv:2003.13815v32020
Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos, Shane Barratt +3
cs.LGmath.OCstat.MLarXiv:1910.12430v12019
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
Maziar Raissi
stat.MLcs.LGmath.AParXiv:1801.06637v12018
Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh +3
stat.MLcs.LGarXiv:1905.12022v12019
PyOD: A Python Toolbox for Scalable Outlier Detection
Yue Zhao, Zain Nasrullah, Zheng Li
cs.LGcs.IRstat.MLarXiv:1901.01588v22019
Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital
Yasunobu Nohara, Koutarou Matsumoto, Hidehisa Soejima +1
cs.LGstat.MLarXiv:2112.11071v22021
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets
Frederik Kratzert, Daniel Klotz, Guy Shalev +3
cs.LGstat.MLarXiv:1907.08456v22019
Is Q-learning Provably Efficient?
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck +1
cs.LGcs.AImath.OCarXiv:1807.03765v12018
Maximum Likelihood Training of Score-Based Diffusion Models
Yang Song, Conor Durkan, Iain Murray +1
stat.MLcs.LGarXiv:2101.09258v42021
Interpolation Consistency Training for Semi-Supervised Learning
Vikas Verma, Kenji Kawaguchi, Alex Lamb +4
stat.MLcs.AIcs.LGarXiv:1903.03825v52019
Auto-Keras: An Efficient Neural Architecture Search System
Haifeng Jin, Qingquan Song, Xia Hu
cs.LGcs.AIstat.MLarXiv:1806.10282v32018
Compressive Transformers for Long-Range Sequence Modelling
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar +1
cs.LGstat.MLarXiv:1911.05507v12019
Deep Learning using Linear Support Vector Machines
Yichuan Tang
cs.LGstat.MLarXiv:1306.0239v42013
Grammar as a Foreign Language
Oriol Vinyals, Lukasz Kaiser, Terry Koo +3
cs.CLcs.LGstat.MLarXiv:1412.7449v32014
Graph Structure Learning for Robust Graph Neural Networks
Wei Jin, Yao Ma, Xiaorui Liu +3
cs.LGcs.CRcs.SIarXiv:2005.10203v32020
Compressed Sensing using Generative Models
Ashish Bora, Ajil Jalal, Eric Price +1
stat.MLcs.ITcs.LGarXiv:1703.03208v12017
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
cs.AIcs.LGstat.MLarXiv:1610.03295v12016
Machine Learning Methods Economists Should Know About
Susan Athey, Guido Imbens
econ.EMstat.MLarXiv:1903.10075v12019
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling +13
cs.LGcs.AIcs.DBarXiv:1811.12823v52018
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Chuxu Zhang, Dongjin Song, Yuncong Chen +7
cs.LGstat.MLarXiv:1811.08055v12018
Generative Moment Matching Networks
Yujia Li, Kevin Swersky, Richard Zemel
cs.LGcs.AIstat.MLarXiv:1502.02761v12015
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag +5
cs.LGcs.CRcs.CVarXiv:2010.09670v32020
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
Matthew Riemer, Ignacio Cases, Robert Ajemian +4
cs.LGcs.AIstat.MLarXiv:1810.11910v32018
Efficient Neural Network Robustness Certification with General Activation Functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen +2
cs.LGcs.CRstat.MLarXiv:1811.00866v12018
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
Ilya Kostrikov, Denis Yarats, Rob Fergus
cs.LGcs.CVeess.IVarXiv:2004.13649v42020
BRITS: Bidirectional Recurrent Imputation for Time Series
Wei Cao, Dong Wang, Jian Li +3
cs.LGstat.MLarXiv:1805.10572v12018
Imaging Time-Series to Improve Classification and Imputation
Zhiguang Wang, Tim Oates
cs.LGcs.NEstat.MLarXiv:1506.00327v12015
Hierarchical Representations for Efficient Architecture Search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals +2
cs.LGcs.CVcs.NEarXiv:1711.00436v22017
Adversarial Attacks on Neural Network Policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow +2
cs.LGcs.CRstat.MLarXiv:1702.02284v12017
MOPO: Model-based Offline Policy Optimization
Tianhe Yu, Garrett Thomas, Lantao Yu +5
cs.LGcs.AIstat.MLarXiv:2005.13239v62020
Grammar Variational Autoencoder
Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
stat.MLarXiv:1703.01925v12017
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
Felix Last, Georgios Douzas, Fernando Bacao
cs.LGstat.MLarXiv:1711.00837v22017
What do we need to build explainable AI systems for the medical domain?
Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis +1
cs.AIstat.MLarXiv:1712.09923v12017
WaveGrad: Estimating Gradients for Waveform Generation
Nanxin Chen, Yu Zhang, Heiga Zen +3
eess.AScs.LGcs.SDarXiv:2009.00713v22020
Contrastive Representation Learning: A Framework and Review
Phuc H. Le-Khac, Graham Healy, Alan F. Smeaton
cs.LGstat.MLarXiv:2010.05113v22020
Data Poisoning Attacks Against Federated Learning Systems
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy +1
cs.LGcs.CRstat.MLarXiv:2007.08432v22020
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee +1
stat.MLcs.LGarXiv:1711.09325v32017
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin +2
stat.MLcs.AIcs.LGarXiv:1802.10026v42018
Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, J. Zico Kolter
cs.LGstat.MLarXiv:2002.11569v22020
Model-Agnostic Interpretability of Machine Learning
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
stat.MLcs.LGarXiv:1606.05386v12016
Benign Overfitting in Linear Regression
Peter L. Bartlett, Philip M. Long, Gábor Lugosi +1
stat.MLcs.LGmath.STarXiv:1906.11300v32019
Unity: A General Platform for Intelligent Agents
Arthur Juliani, Vincent-Pierre Berges, Ervin Teng +8
cs.LGcs.AIcs.NEarXiv:1809.02627v22018
A Survey on the Explainability of Supervised Machine Learning
Nadia Burkart, Marco F. Huber
cs.LGcs.AIstat.MLarXiv:2011.07876v12020
Understanding disentangling in $β$-VAE
Christopher P. Burgess, Irina Higgins, Arka Pal +4
stat.MLcs.AIcs.LGarXiv:1804.03599v12018
How does Disagreement Help Generalization against Label Corruption?
Xingrui Yu, Bo Han, Jiangchao Yao +3
cs.LGstat.MLarXiv:1901.04215v32019
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish +1
cs.LGstat.MLarXiv:1812.00564v12018
Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac +1
cs.LGq-bio.QMstat.MLarXiv:2203.17003v22022
Detecting Adversarial Samples from Artifacts
Reuben Feinman, Ryan R. Curtin, Saurabh Shintre +1
stat.MLcs.LGarXiv:1703.00410v32017
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro +2
cs.LGcs.AIstat.MEarXiv:2108.13264v42021

Machine Learning (stat)

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

Transfer Learning in Deep Reinforcement Learning: A Survey

Unitary Evolution Recurrent Neural Networks

Causal Effect Inference with Deep Latent-Variable Models

Up or Down? Adaptive Rounding for Post-Training Quantization

Kernel Mean Embedding of Distributions: A Review and Beyond

Crowdsourcing Multiple Choice Science Questions

Pseudo Numerical Methods for Diffusion Models on Manifolds

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Temporal Pattern Attention for Multivariate Time Series Forecasting

Hypergraph Convolution and Hypergraph Attention

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Differentiable Convex Optimization Layers

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

Bayesian Nonparametric Federated Learning of Neural Networks

PyOD: A Python Toolbox for Scalable Outlier Detection

Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital

Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets

Is Q-learning Provably Efficient?

Maximum Likelihood Training of Score-Based Diffusion Models

Interpolation Consistency Training for Semi-Supervised Learning

Auto-Keras: An Efficient Neural Architecture Search System

Compressive Transformers for Long-Range Sequence Modelling

Deep Learning using Linear Support Vector Machines

Grammar as a Foreign Language

Graph Structure Learning for Robust Graph Neural Networks

Compressed Sensing using Generative Models

Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Machine Learning Methods Economists Should Know About

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

Generative Moment Matching Networks

RobustBench: a standardized adversarial robustness benchmark

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Efficient Neural Network Robustness Certification with General Activation Functions

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

BRITS: Bidirectional Recurrent Imputation for Time Series

Imaging Time-Series to Improve Classification and Imputation

Hierarchical Representations for Efficient Architecture Search

Adversarial Attacks on Neural Network Policies

MOPO: Model-based Offline Policy Optimization

Grammar Variational Autoencoder

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

What do we need to build explainable AI systems for the medical domain?

WaveGrad: Estimating Gradients for Waveform Generation

Contrastive Representation Learning: A Framework and Review

Data Poisoning Attacks Against Federated Learning Systems

Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

Overfitting in adversarially robust deep learning

Model-Agnostic Interpretability of Machine Learning

Benign Overfitting in Linear Regression

Unity: A General Platform for Intelligent Agents

A Survey on the Explainability of Supervised Machine Learning

Understanding disentangling in $β$-VAE

How does Disagreement Help Generalization against Label Corruption?

Split learning for health: Distributed deep learning without sharing raw patient data

Equivariant Diffusion for Molecule Generation in 3D

Detecting Adversarial Samples from Artifacts

Deep Reinforcement Learning at the Edge of the Statistical Precipice