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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5,821 to 5,880 of 6,792

Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
Daniel Stoller, Sebastian Ewert, Simon Dixon
cs.SDeess.ASstat.MLarXiv:1806.03185v12018
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
Lam M. Nguyen, Jie Liu, Katya Scheinberg +1
stat.MLcs.LGmath.OCarXiv:1703.00102v22017
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
cs.LGcs.AIcs.CVarXiv:1412.6614v42014
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Sebastian Raschka, Joshua Patterson, Corey Nolet
cs.LGstat.MLarXiv:2002.04803v22020
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
stat.MLcs.LGarXiv:1406.2541v12014
Convergence rates of efficient global optimization algorithms
Adam D. Bull
stat.MLmath.OCmath.STarXiv:1101.3501v32011
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
cs.LGstat.MLarXiv:2007.13518v42020
VAE with a VampPrior
Jakub M. Tomczak, Max Welling
cs.LGcs.AIstat.MLarXiv:1705.07120v52017
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Maryam Fazel, Rong Ge, Sham M. Kakade +1
cs.LGstat.MLarXiv:1801.05039v32018
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Christopher Hesse, Jacob Hilton +1
cs.LGstat.MLarXiv:1912.01588v22019
The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies
David M. Blei, Thomas L. Griffiths, Michael I. Jordan
stat.MLarXiv:0710.0845v32007
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Curtis G. Northcutt, Anish Athalye, Jonas Mueller
stat.MLcs.AIcs.LGarXiv:2103.14749v42021
Deep Evidential Regression
Alexander Amini, Wilko Schwarting, Ava Soleimany +1
cs.LGcs.NEstat.MLarXiv:1910.02600v22019
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver +1
cs.LGcs.AIstat.MLarXiv:1806.06923v12018
DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
Mike Lambeta, Po-Wei Chou, Stephen Tian +9
cs.ROcs.LGeess.SYarXiv:2005.14679v12020
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo +7
cs.LGeess.IVstat.MLarXiv:1904.07773v62019
A comparative study of fairness-enhancing interventions in machine learning
Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian +3
stat.MLcs.CYcs.LGarXiv:1802.04422v12018
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology
Alexei Botchkarev
stat.MEcs.LGstat.MLarXiv:1809.03006v12018
Deep Learning-Based Channel Estimation
Mehran Soltani, Vahid Pourahmadi, Ali Mirzaei +1
cs.ITcs.LGeess.SParXiv:1810.05893v42018
Semi-Supervised Learning with Generative Adversarial Networks
Augustus Odena
stat.MLcs.LGarXiv:1606.01583v22016
Just Train Twice: Improving Group Robustness without Training Group Information
Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen +5
cs.LGcs.AIcs.CYarXiv:2107.09044v22021
Token-Level Likelihood-Array Regression for Membership Inference and AI-Generated Text Detection
Jiajun Sun, Zhanrui Cai
stat.MLcs.LGstat.MEarXiv:2608.22179v12026
Reward Constrained Policy Optimization
Chen Tessler, Daniel J. Mankowitz, Shie Mannor
cs.LGcs.AIstat.MLarXiv:1805.11074v32018
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent
cs.LGcs.SDstat.MLarXiv:1206.6392v12012
Reducing Transformer Depth on Demand with Structured Dropout
Angela Fan, Edouard Grave, Armand Joulin
cs.LGcs.CLstat.MLarXiv:1909.11556v12019
Learning Generative Models with Sinkhorn Divergences
Aude Genevay, Gabriel Peyré, Marco Cuturi
stat.MLarXiv:1706.00292v32017
Scalable Variational Gaussian Process Classification
James Hensman, Alex Matthews, Zoubin Ghahramani
stat.MLarXiv:1411.2005v12014
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob R Gardner +2
cs.LGstat.MLarXiv:1910.01739v42019
Research Priorities for Robust and Beneficial Artificial Intelligence
Stuart Russell, Daniel Dewey, Max Tegmark
cs.AIstat.MLarXiv:1602.03506v12016
Order-independent constraint-based causal structure learning
Diego Colombo, Marloes H. Maathuis
stat.MLcs.LGarXiv:1211.3295v22012
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer +6
cs.CVcs.LGcs.NEarXiv:1806.05034v42018
Deep Learning for Computational Chemistry
Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu
stat.MLcs.AIcs.CEarXiv:1701.04503v12017
Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen +2
cs.LGcs.AIcs.CVarXiv:1811.00995v32018
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Akash Srivastava, Lazar Valkov, Chris Russell +2
stat.MLarXiv:1705.07761v32017
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn, Kelvin Xu, Sergey Levine
cs.LGcs.AIstat.MLarXiv:1806.02817v22018
A Little Is Enough: Circumventing Defenses For Distributed Learning
Moran Baruch, Gilad Baruch, Yoav Goldberg
cs.LGcs.CRcs.DCarXiv:1902.06156v12019
Layer by layer, module by module: Choose both for optimal OOD probing of ViT
Ambroise Odonnat, Vasilii Feofanov, Laetitia Chapel +2
cs.CVcs.LGstat.MLarXiv:2603.05280v12026
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo +4
cs.LGcs.NEstat.MLarXiv:1611.02648v22016
Kernel-based Conditional Independence Test and Application in Causal Discovery
Kun Zhang, Jonas Peters, Dominik Janzing +1
cs.LGstat.MLarXiv:1202.3775v12012
Entropy Search for Information-Efficient Global Optimization
Philipp Hennig, Christian J. Schuler
stat.MLcs.AIarXiv:1112.1217v12011
Neural Text Generation with Unlikelihood Training
Sean Welleck, Ilia Kulikov, Stephen Roller +3
cs.LGcs.CLstat.MLarXiv:1908.04319v22019
Cross-validation failure: small sample sizes lead to large error bars
Gaël Varoquaux
q-bio.QMstat.MEstat.MLarXiv:1706.07581v12017
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Nan Jiang, Lihong Li
cs.LGcs.AIeess.SYarXiv:1511.03722v32015
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini +2
stat.MLcs.CLcs.LGarXiv:2102.05379v32021
MLPerf Inference Benchmark
Vijay Janapa Reddi, Christine Cheng, David Kanter +44
cs.LGcs.PFstat.MLarXiv:1911.02549v22019
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou +5
cs.ROcs.CVcs.LGarXiv:1809.10732v22018
Fantastic Generalization Measures and Where to Find Them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi +2
cs.LGstat.MLarXiv:1912.02178v12019
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong +1
cs.LGstat.MLarXiv:2007.08176v22020
Towards Deeper Graph Neural Networks
Meng Liu, Hongyang Gao, Shuiwang Ji
cs.LGstat.MLarXiv:2007.09296v12020
Rethinking Pre-training and Self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin +4
cs.CVcs.LGstat.MLarXiv:2006.06882v22020
Change Detection in Probability Flow ODE: Online Testing in Diffusion Latent Spaces
Artem Kraevskiy, Artem Prokhorov
cs.LGstat.MLarXiv:2608.22807v22026
Rotation Equivariant CNNs for Digital Pathology
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens +2
cs.CVcs.LGstat.MLarXiv:1806.03962v12018
Adversarial Attacks and Defences: A Survey
Anirban Chakraborty, Manaar Alam, Vishal Dey +2
cs.LGcs.CRstat.MLarXiv:1810.00069v12018
Chain-of-Trajectories: Unlocking the Intrinsic Generative Optimality of Diffusion Models via Graph-Theoretic Planning
Ping Chen, Xiang Liu, Xingpeng Zhang +7
cs.LGcs.CVstat.MLarXiv:2603.14704v12026
WeatherBench: A benchmark dataset for data-driven weather forecasting
Stephan Rasp, Peter D. Dueben, Sebastian Scher +3
physics.ao-phstat.MLarXiv:2002.00469v32020
Riemannian Motion Generation: A Unified Framework for Human Motion Representation and Generation via Riemannian Flow Matching
Fangran Miao, Jian Huang, Ting Li
cs.CVstat.MLarXiv:2603.15016v12026
Debiased Contrastive Learning
Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen +2
cs.LGstat.MLarXiv:2007.00224v32020
Neural Variational Inference and Learning in Belief Networks
Andriy Mnih, Karol Gregor
cs.LGstat.MLarXiv:1402.0030v22014
Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization
Jun Liu, Shuiwang Ji, Jieping Ye
cs.LGcs.CVstat.MLarXiv:1205.2631v12012
FSD50K: An Open Dataset of Human-Labeled Sound Events
Eduardo Fonseca, Xavier Favory, Jordi Pons +2
cs.SDcs.LGeess.ASarXiv:2010.00475v22020

Machine Learning (stat)

Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

Convergence rates of efficient global optimization algorithms

FedML: A Research Library and Benchmark for Federated Machine Learning

VAE with a VampPrior

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator

Leveraging Procedural Generation to Benchmark Reinforcement Learning

The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies

Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

Deep Evidential Regression

Implicit Quantile Networks for Distributional Reinforcement Learning

DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation

Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation

A comparative study of fairness-enhancing interventions in machine learning

Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology

Deep Learning-Based Channel Estimation

Semi-Supervised Learning with Generative Adversarial Networks

Just Train Twice: Improving Group Robustness without Training Group Information

Token-Level Likelihood-Array Regression for Membership Inference and AI-Generated Text Detection

Reward Constrained Policy Optimization

Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription

Reducing Transformer Depth on Demand with Structured Dropout

Learning Generative Models with Sinkhorn Divergences

Scalable Variational Gaussian Process Classification

Scalable Global Optimization via Local Bayesian Optimization

Research Priorities for Robust and Beneficial Artificial Intelligence

Order-independent constraint-based causal structure learning

A Probabilistic U-Net for Segmentation of Ambiguous Images

Deep Learning for Computational Chemistry

Invertible Residual Networks

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

Probabilistic Model-Agnostic Meta-Learning

A Little Is Enough: Circumventing Defenses For Distributed Learning

Layer by layer, module by module: Choose both for optimal OOD probing of ViT

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

Kernel-based Conditional Independence Test and Application in Causal Discovery

Entropy Search for Information-Efficient Global Optimization

Neural Text Generation with Unlikelihood Training

Cross-validation failure: small sample sizes lead to large error bars

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

MLPerf Inference Benchmark

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

Fantastic Generalization Measures and Where to Find Them

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Towards Deeper Graph Neural Networks

Rethinking Pre-training and Self-training

Change Detection in Probability Flow ODE: Online Testing in Diffusion Latent Spaces

Rotation Equivariant CNNs for Digital Pathology

Adversarial Attacks and Defences: A Survey

Chain-of-Trajectories: Unlocking the Intrinsic Generative Optimality of Diffusion Models via Graph-Theoretic Planning

WeatherBench: A benchmark dataset for data-driven weather forecasting

Riemannian Motion Generation: A Unified Framework for Human Motion Representation and Generation via Riemannian Flow Matching

Debiased Contrastive Learning

Neural Variational Inference and Learning in Belief Networks

Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization

FSD50K: An Open Dataset of Human-Labeled Sound Events