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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4,801 to 4,860 of 6,772

Neural Episodic Control
Alexander Pritzel, Benigno Uria, Sriram Srinivasan +5
cs.LGstat.MLarXiv:1703.01988v12017
Unsupervised Learning of 3D Structure from Images
Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed +3
cs.CVcs.LGstat.MLarXiv:1607.00662v22016
Measuring abstract reasoning in neural networks
David G. T. Barrett, Felix Hill, Adam Santoro +2
cs.LGstat.MLarXiv:1807.04225v12018
Transformer Hawkes Process
Simiao Zuo, Haoming Jiang, Zichong Li +2
cs.LGstat.MLarXiv:2002.09291v52020
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin Woo Kim +1
cs.LGstat.MLarXiv:1806.05805v12018
A Review of Challenges and Opportunities in Machine Learning for Health
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam +3
cs.LGcs.CYstat.MLarXiv:1806.00388v42018
Protection Against Reconstruction and Its Applications in Private Federated Learning
Abhishek Bhowmick, John Duchi, Julien Freudiger +2
stat.MLcs.LGarXiv:1812.00984v22018
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Amir-Hossein Karimi, Gilles Barthe, Borja Balle +1
cs.LGcs.AIcs.LOarXiv:1905.11190v52019
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
Yuchen Zhang, John C. Duchi, Martin J. Wainwright
math.STcs.LGstat.MLarXiv:1305.5029v22013
Stochastic Gradient Push for Distributed Deep Learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas +1
cs.LGcs.AIcs.DCarXiv:1811.10792v32018
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri +3
cs.LGcs.DCmath.OCarXiv:1910.06378v42019
Batch Policy Learning under Constraints
Hoang M. Le, Cameron Voloshin, Yisong Yue
cs.LGcs.AImath.OCarXiv:1903.08738v12019
Graph Neural Networks with Heterophily
Jiong Zhu, Ryan A. Rossi, Anup Rao +4
cs.LGcs.SIstat.MLarXiv:2009.13566v32020
Neural Optimizer Search with Reinforcement Learning
Irwan Bello, Barret Zoph, Vijay Vasudevan +1
cs.AIcs.LGstat.MLarXiv:1709.07417v22017
Improving Palliative Care with Deep Learning
Anand Avati, Kenneth Jung, Stephanie Harman +3
cs.CYcs.LGstat.MLarXiv:1711.06402v12017
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Allison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt
cs.CYcs.LGstat.MLarXiv:1710.11214v22017
Linearly-Recurrent Autoencoder Networks for Learning Dynamics
Samuel E. Otto, Clarence W. Rowley
math.DScs.LGstat.MLarXiv:1712.01378v22017
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
Ryan Turner, David Eriksson, Michael McCourt +4
cs.LGcs.AIstat.MLarXiv:2104.10201v22021
Runaway Feedback Loops in Predictive Policing
Danielle Ensign, Sorelle A. Friedler, Scott Neville +2
cs.CYstat.MLarXiv:1706.09847v32017
Variable importance in binary regression trees and forests
Hemant Ishwaran
stat.MLarXiv:0711.2434v12007
Compositionality decomposed: how do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul +1
cs.CLcs.AIcs.LGarXiv:1908.08351v22019
Variance Reduction for Faster Non-Convex Optimization
Zeyuan Allen-Zhu, Elad Hazan
math.OCcs.DScs.LGarXiv:1603.05643v22016
A Survey on Anomaly Detection for Technical Systems using LSTM Networks
Benjamin Lindemann, Benjamin Maschler, Nada Sahlab +1
cs.LGcs.AIstat.MLarXiv:2105.13810v12021
GP-VAE: Deep Probabilistic Time Series Imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch +1
stat.MLcs.LGarXiv:1907.04155v52019
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Bryan Wilder, Bistra Dilkina, Milind Tambe
cs.LGcs.AIstat.MLarXiv:1809.05504v22018
A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation
Jalaj Bhandari, Daniel Russo, Raghav Singal
cs.LGstat.MLarXiv:1806.02450v22018
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner +1
stat.MLcs.LGcs.SIarXiv:1803.00816v22018
Learning Disentangled Representations for Recommendation
Jianxin Ma, Chang Zhou, Peng Cui +2
cs.LGcs.IRstat.MLarXiv:1910.14238v12019
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song +6
cs.LGstat.MLarXiv:1910.00760v32019
Revisiting k-means: New Algorithms via Bayesian Nonparametrics
Brian Kulis, Michael I. Jordan
cs.LGstat.MLarXiv:1111.0352v22011
A Variational Inequality Perspective on Generative Adversarial Networks
Gauthier Gidel, Hugo Berard, Gaëtan Vignoud +2
cs.LGmath.OCstat.MLarXiv:1802.10551v52018
Fixup Initialization: Residual Learning Without Normalization
Hongyi Zhang, Yann N. Dauphin, Tengyu Ma
cs.LGcs.CVstat.MLarXiv:1901.09321v22019
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
Yunsheng Bai, Hao Ding, Song Bian +3
cs.LGstat.MLarXiv:1808.05689v42018
An Accurate and Single-Communication Federated Inference Algorithm
Laura Montagnani, Anthony CC Coolen, Marianne A Jonker
stat.MEmath.STstat.COarXiv:2608.27063v12026
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Edward De Brouwer, Jaak Simm, Adam Arany +1
cs.LGstat.MLarXiv:1905.12374v22019
Hybrid Deterministic-Stochastic Methods for Data Fitting
Michael P. Friedlander, Mark Schmidt
math.NAeess.SYmath.OCarXiv:1104.2373v42011
Global Optimality of Local Search for Low Rank Matrix Recovery
Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
stat.MLcs.LGmath.OCarXiv:1605.07221v22016
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert +10
cs.LGstat.MLarXiv:2004.10240v22020
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Sana Tonekaboni, Danny Eytan, Anna Goldenberg
cs.LGstat.MLarXiv:2106.00750v12021
A Dual Approach to Scalable Verification of Deep Networks
Krishnamurthy, Dvijotham, Robert Stanforth +3
cs.LGstat.MLarXiv:1803.06567v22018
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings
Nicholas Rhinehart, Rowan McAllister, Kris Kitani +1
cs.CVcs.AIcs.LGarXiv:1905.01296v32019
Mnemonics Training: Multi-Class Incremental Learning without Forgetting
Yaoyao Liu, Yuting Su, An-An Liu +2
cs.CVstat.MLarXiv:2002.10211v62020
Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, Ji Xu
cs.LGstat.MLarXiv:1903.07571v22019
Verifiable Reinforcement Learning via Policy Extraction
Osbert Bastani, Yewen Pu, Armando Solar-Lezama
cs.LGstat.MLarXiv:1805.08328v22018
Generative Adversarial Perturbations
Omid Poursaeed, Isay Katsman, Bicheng Gao +1
cs.CVcs.CRcs.LGarXiv:1712.02328v32017
Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks
Jean Hausser, Korbinian Strimmer
stat.MLarXiv:0811.3579v32008
ReZero is All You Need: Fast Convergence at Large Depth
Thomas Bachlechner, Bodhisattwa Prasad Majumder, Huanru Henry Mao +2
cs.LGcs.CLstat.MLarXiv:2003.04887v22020
Insights into Performance Fitness and Error Metrics for Machine Learning
M. Z. Naser, Amir Alavi
cs.LGphysics.data-anstat.MLarXiv:2006.00887v12020
Training behavior of deep neural network in frequency domain
Zhi-Qin John Xu, Yaoyu Zhang, Yanyang Xiao
cs.LGcs.AIcs.ITarXiv:1807.01251v62018
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
Huan Zhang, Hongge Chen, Chaowei Xiao +4
cs.LGstat.MLarXiv:2003.08938v72020
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Ekagra Ranjan, Soumya Sanyal, Partha Pratim Talukdar
cs.LGstat.MLarXiv:1911.07979v32019
Understanding and Robustifying Differentiable Architecture Search
Arber Zela, Thomas Elsken, Tonmoy Saikia +3
cs.LGcs.AIcs.CVarXiv:1909.09656v22019
Data Augmentation Can Improve Robustness
Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian +3
cs.CVcs.LGstat.MLarXiv:2111.05328v12021
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Matthias Feurer, Katharina Eggensperger, Stefan Falkner +2
cs.LGstat.MLarXiv:2007.04074v32020
Batch Bayesian Optimization via Local Penalization
Javier González, Zhenwen Dai, Philipp Hennig +1
stat.MLarXiv:1505.08052v42015
Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach
Lei Lin, Zhengbing He, Srinivas Peeta
stat.MLcs.LGarXiv:1712.04997v22017
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
Yuichi Yoshida, Takeru Miyato
stat.MLcs.LGarXiv:1705.10941v12017
Exponentially Weighted Moving Average Charts for Detecting Concept Drift
Gordon J. Ross, Niall M. Adams, Dimitris K. Tasoulis +1
stat.MLcs.LGstat.AParXiv:1212.6018v12012
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Maximilian Karl, Maximilian Soelch, Justin Bayer +1
stat.MLcs.LGeess.SYarXiv:1605.06432v32016
A Survey of Deep Meta-Learning
Mike Huisman, Jan N. van Rijn, Aske Plaat
cs.LGcs.AIstat.MLarXiv:2010.03522v22020

Machine Learning (stat)

Neural Episodic Control

Unsupervised Learning of 3D Structure from Images

Measuring abstract reasoning in neural networks

Transformer Hawkes Process

Molecular generative model based on conditional variational autoencoder for de novo molecular design

A Review of Challenges and Opportunities in Machine Learning for Health

Protection Against Reconstruction and Its Applications in Private Federated Learning

Model-Agnostic Counterfactual Explanations for Consequential Decisions

Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates

Stochastic Gradient Push for Distributed Deep Learning

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

Batch Policy Learning under Constraints

Graph Neural Networks with Heterophily

Neural Optimizer Search with Reinforcement Learning

Improving Palliative Care with Deep Learning

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Linearly-Recurrent Autoencoder Networks for Learning Dynamics

Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020

Runaway Feedback Loops in Predictive Policing

Variable importance in binary regression trees and forests

Compositionality decomposed: how do neural networks generalise?

Variance Reduction for Faster Non-Convex Optimization

A Survey on Anomaly Detection for Technical Systems using LSTM Networks

GP-VAE: Deep Probabilistic Time Series Imputation

Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation

NetGAN: Generating Graphs via Random Walks

Learning Disentangled Representations for Recommendation

Efficient Graph Generation with Graph Recurrent Attention Networks

Revisiting k-means: New Algorithms via Bayesian Nonparametrics

A Variational Inequality Perspective on Generative Adversarial Networks

Fixup Initialization: Residual Learning Without Normalization

SimGNN: A Neural Network Approach to Fast Graph Similarity Computation

An Accurate and Single-Communication Federated Inference Algorithm

GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series

Hybrid Deterministic-Stochastic Methods for Data Fitting

Global Optimality of Local Search for Low Rank Matrix Recovery

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding

A Dual Approach to Scalable Verification of Deep Networks

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

Mnemonics Training: Multi-Class Incremental Learning without Forgetting

Two models of double descent for weak features

Verifiable Reinforcement Learning via Policy Extraction

Generative Adversarial Perturbations

Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

ReZero is All You Need: Fast Convergence at Large Depth

Insights into Performance Fitness and Error Metrics for Machine Learning

Training behavior of deep neural network in frequency domain

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

Understanding and Robustifying Differentiable Architecture Search

Data Augmentation Can Improve Robustness

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

Batch Bayesian Optimization via Local Penalization

Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach

Spectral Norm Regularization for Improving the Generalizability of Deep Learning

Exponentially Weighted Moving Average Charts for Detecting Concept Drift

Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

A Survey of Deep Meta-Learning