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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2,521 to 2,580 of 6,786

Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
Paulo R. de O. da Costa, Jason Rhuggenaath, Yingqian Zhang +1
cs.LGcs.AIstat.MLarXiv:2004.01608v32020
A Simple Neural Attentive Meta-Learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen +1
cs.AIcs.LGcs.NEarXiv:1707.03141v32017
Intriguing Properties of Contrastive Losses
Ting Chen, Calvin Luo, Lala Li
cs.LGcs.AIcs.CVarXiv:2011.02803v32020
TSLANet: Rethinking Transformers for Time Series Representation Learning
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +2
cs.LGstat.MLarXiv:2404.08472v22024
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
Chunyuan Li, Xiang Gao, Yuan Li +4
cs.CLcs.LGstat.MLarXiv:2004.04092v42020
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
A. Chatzimparmpas, R. Martins, I. Jusufi +3
cs.LGcs.HCstat.MLarXiv:2212.11737v22022
Online Non-Monotone DR-Submodular Maximization Matching the Offline $0.401$ Factor
Vaneet Aggarwal, Yiyang Lu
cs.LGcs.AIcs.CCarXiv:2609.02145v12026
Open-ended Learning in Symmetric Zero-sum Games
David Balduzzi, Marta Garnelo, Yoram Bachrach +4
cs.LGcs.GTcs.MAarXiv:1901.08106v22019
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
cs.LGcs.CRcs.CVarXiv:1906.10908v22019
Categorizing Variants of Goodhart's Law
David Manheim, Scott Garrabrant
cs.AIq-fin.GNstat.MLarXiv:1803.04585v42018
Practical Coreset Constructions for Machine Learning
Olivier Bachem, Mario Lucic, Andreas Krause
stat.MLarXiv:1703.06476v22017
Conditional Generative Neural System for Probabilistic Trajectory Prediction
Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
cs.CVcs.AIcs.LGarXiv:1905.01631v22019
Generative Adversarial Active Learning
Jia-Jie Zhu, José Bento
cs.LGstat.MLarXiv:1702.07956v52017
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Matteo Turchetta, Felix Berkenkamp, Andreas Krause
cs.LGcs.AIcs.ROarXiv:1606.04753v22016
Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi +1
cs.LGstat.MLarXiv:1705.10257v22017
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
Jongsoo Park, Maxim Naumov, Protonu Basu +25
cs.LGstat.MLarXiv:1811.09886v22018
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
Alexander G. de G. Matthews, James Hensman, Richard E. Turner +1
stat.MLarXiv:1504.07027v22015
Class-Incremental Continual Learning into the eXtended DER-verse
Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega +2
cs.LGstat.MLarXiv:2201.00766v22022
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
Amos Beimel, Kobbi Nissim, Uri Stemmer
cs.LGcs.CRstat.MLarXiv:1407.2674v12014
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Michihiro Yasunaga, Percy Liang
cs.SEcs.CLcs.LGarXiv:2005.10636v22020
Lightweight Probabilistic Deep Networks
Jochen Gast, Stefan Roth
cs.CVcs.LGstat.MLarXiv:1805.11327v12018
Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
Li Huang, Lei Wang
physics.comp-phcond-mat.str-elstat.MLarXiv:1610.02746v22016
Pooling and Drift in Delayed Bandits
Melika Baghi
stat.MLcs.LGarXiv:2609.01761v12026
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
Tengyang Xie, Nan Jiang, Huan Wang +2
cs.LGstat.MLarXiv:2106.04895v22021
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
Runtian Zhai, Chen Dan, Di He +5
cs.LGcs.CRstat.MLarXiv:2001.02378v42020
Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction
Tianyi Zhou, Dacheng Tao, Xindong Wu
cs.LGstat.MLarXiv:1007.3564v32010
Molecular graph generation with Graph Neural Networks
Pietro Bongini, Monica Bianchini, Franco Scarselli
stat.MLcs.LGq-bio.BMarXiv:2012.07397v22020
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager +3
cs.LGcs.AIstat.MLarXiv:1806.10729v52018
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization
Tomaso Poggio, Andrzej Banburski, Qianli Liao
cs.LGstat.MLarXiv:1908.09375v12019
Automated Machine Learning: State-of-The-Art and Open Challenges
Radwa Elshawi, Mohamed Maher, Sherif Sakr
cs.LGstat.MLarXiv:1906.02287v22019
Deep Factors for Forecasting
Yuyang Wang, Alex Smola, Danielle C. Maddix +3
stat.MLcs.LGarXiv:1905.12417v12019
Unsupervised Predictive Memory in a Goal-Directed Agent
Greg Wayne, Chia-Chun Hung, David Amos +21
cs.LGstat.MLarXiv:1803.10760v12018
MLI: An API for Distributed Machine Learning
Evan R. Sparks, Ameet Talwalkar, Virginia Smith +6
cs.LGcs.DCstat.MLarXiv:1310.5426v22013
Sampling Permutations for Shapley Value Estimation
Rory Mitchell, Joshua Cooper, Eibe Frank +1
stat.MLcs.LGmath.COarXiv:2104.12199v22021
Transferring Subspaces Between Subjects in Brain-Computer Interfacing
Wojciech Samek, Frank C. Meinecke, Klaus-Robert Müller
stat.MLcs.HCcs.LGarXiv:1209.4115v22012
Robust Bayesian Inference for Unnormalized Models with Mixed-Domain Data
Jiongran Wang, Debdeep Pati, Anirban Bhattacharya
stat.MEstat.MLarXiv:2609.01783v12026
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem
André Hottung, Kevin Tierney
cs.AIstat.MLarXiv:1911.09539v22019
Faster independent component analysis by preconditioning with Hessian approximations
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
stat.MLstat.AParXiv:1706.08171v32017
Label Sanitization against Label Flipping Poisoning Attacks
Andrea Paudice, Luis Muñoz-González, Emil C. Lupu
stat.MLcs.CRcs.LGarXiv:1803.00992v22018
Defending Neural Backdoors via Generative Distribution Modeling
Ximing Qiao, Yukun Yang, Hai Li
cs.LGstat.MLarXiv:1910.04749v22019
Interpolation-Prediction Networks for Irregularly Sampled Time Series
Satya Narayan Shukla, Benjamin M. Marlin
cs.LGstat.MLarXiv:1909.07782v12019
Highly accurate model for prediction of lung nodule malignancy with CT scans
Jason Causey, Junyu Zhang, Shiqian Ma +6
cs.CVq-bio.QMstat.MLarXiv:1802.01756v12018
Summaries:한국어
Barzilai-Borwein Step Size for Stochastic Gradient Descent
Conghui Tan, Shiqian Ma, Yu-Hong Dai +1
math.OCcs.LGstat.MLarXiv:1605.04131v22016
Adversarial Reprogramming of Neural Networks
Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein
cs.LGcs.CRcs.CVarXiv:1806.11146v22018
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
Manqing Dong, Feng Yuan, Lina Yao +2
cs.IRcs.LGstat.MLarXiv:2007.03183v12020
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Liantao Ma, Chaohe Zhang, Yasha Wang +6
cs.LGstat.MLarXiv:1911.12216v12019
Self-supervised Learning on Graphs: Deep Insights and New Direction
Wei Jin, Tyler Derr, Haochen Liu +4
cs.LGstat.MLarXiv:2006.10141v12020
$QD$-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations
Soummya Kar, Jose' M. F. Moura, H. Vincent Poor
stat.MLcs.LGcs.MAarXiv:1205.0047v22012
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Jakob Runge
stat.MLcs.ITstat.MEarXiv:1709.01447v12017
Factoring nonnegative matrices with linear programs
Victor Bittorf, Benjamin Recht, Christopher Re +1
math.OCcs.LGstat.MLarXiv:1206.1270v22012
Kernel Instrumental Variable Regression
Rahul Singh, Maneesh Sahani, Arthur Gretton
cs.LGecon.EMmath.FAarXiv:1906.00232v62019
Signal Processing on Graphs: Causal Modeling of Unstructured Data
Jonathan Mei, José M. F. Moura
cs.ITstat.MLarXiv:1503.00173v62015
Deep Learning Based Regression and Multi-class Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
Youjun Xu, Jianfeng Pei, Luhua Lai
stat.MLcs.LGq-bio.QMarXiv:1704.04718v32017
On the Convergence Rate of Training Recurrent Neural Networks
Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song
cs.LGcs.DScs.NEarXiv:1810.12065v42018
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
Yanshuai Cao, David J. Fleet
cs.LGcs.AIstat.MLarXiv:1410.7827v22014
Phasic Policy Gradient
Karl Cobbe, Jacob Hilton, Oleg Klimov +1
cs.LGstat.MLarXiv:2009.04416v12020
Topological Recurrent Neural Network for Diffusion Prediction
Jia Wang, Vincent W. Zheng, Zemin Liu +1
cs.LGstat.MLarXiv:1711.10162v22017
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
Santiago Silva, Boris Gutman, Eduardo Romero +3
stat.MLcs.LGq-bio.NCarXiv:1810.08553v42018
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
Qianxiao Li, Cheng Tai, Weinan E
cs.LGstat.MLarXiv:1811.01558v12018
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Kevin Bello, Bryon Aragam, Pradeep Ravikumar
cs.LGstat.MEstat.MLarXiv:2209.08037v32022

Machine Learning (stat)

Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning

A Simple Neural Attentive Meta-Learner

Intriguing Properties of Contrastive Losses

TSLANet: Rethinking Transformers for Time Series Representation Learning

Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Online Non-Monotone DR-Submodular Maximization Matching the Offline $0.401$ Factor

Open-ended Learning in Symmetric Zero-sum Games

Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks

Categorizing Variants of Goodhart's Law

Practical Coreset Constructions for Machine Learning

Conditional Generative Neural System for Probabilistic Trajectory Prediction

Generative Adversarial Active Learning

Safe Exploration in Finite Markov Decision Processes with Gaussian Processes

Boltzmann Exploration Done Right

Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes

Class-Incremental Continual Learning into the eXtended DER-verse

Private Learning and Sanitization: Pure vs. Approximate Differential Privacy

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

Lightweight Probabilistic Deep Networks

Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines

Pooling and Drift in Delayed Bandits

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning

MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

Molecular graph generation with Graph Neural Networks

Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation

Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization

Automated Machine Learning: State-of-The-Art and Open Challenges

Deep Factors for Forecasting

Unsupervised Predictive Memory in a Goal-Directed Agent

MLI: An API for Distributed Machine Learning

Sampling Permutations for Shapley Value Estimation

Transferring Subspaces Between Subjects in Brain-Computer Interfacing

Robust Bayesian Inference for Unnormalized Models with Mixed-Domain Data

Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem

Faster independent component analysis by preconditioning with Hessian approximations

Label Sanitization against Label Flipping Poisoning Attacks

Defending Neural Backdoors via Generative Distribution Modeling

Interpolation-Prediction Networks for Irregularly Sampled Time Series

Highly accurate model for prediction of lung nodule malignancy with CT scans

Barzilai-Borwein Step Size for Stochastic Gradient Descent

Adversarial Reprogramming of Neural Networks

MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

Self-supervised Learning on Graphs: Deep Insights and New Direction

$QD$-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations

Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information

Factoring nonnegative matrices with linear programs

Kernel Instrumental Variable Regression

Signal Processing on Graphs: Causal Modeling of Unstructured Data

Deep Learning Based Regression and Multi-class Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

On the Convergence Rate of Training Recurrent Neural Networks

Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions

Phasic Policy Gradient

Topological Recurrent Neural Network for Diffusion Prediction

Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization