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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3,961 to 4,020 of 6,778

Distributed learning of deep neural network over multiple agents
Otkrist Gupta, Ramesh Raskar
cs.LGstat.MLarXiv:1810.06060v12018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Dongkun Zhang, Lu Lu, Ling Guo +1
math.APphysics.comp-phstat.MLarXiv:1809.08327v12018
Handling Incomplete Heterogeneous Data using VAEs
Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani +1
cs.LGcs.AIstat.MLarXiv:1807.03653v42018
Confounding variables can degrade generalization performance of radiological deep learning models
John R. Zech, Marcus A. Badgeley, Manway Liu +3
cs.CVcs.LGstat.MLarXiv:1807.00431v22018
Deep learning in business analytics and operations research: Models, applications and managerial implications
Mathias Kraus, Stefan Feuerriegel, Asil Oztekin
cs.LGstat.MLarXiv:1806.10897v32018
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante +4
stat.MLcs.LGarXiv:1806.01738v12018
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data
Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa +2
stat.MLcs.LGstat.MEarXiv:1803.11266v12018
Robust Loss Functions under Label Noise for Deep Neural Networks
Aritra Ghosh, Himanshu Kumar, P. S. Sastry
stat.MLcs.LGarXiv:1712.09482v12017
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations
Maziar Raissi, Paris Perdikaris, George Em Karniadakis
stat.MLmath.APmath.DSarXiv:1703.10230v12017
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Shixia Liu, Xiting Wang, Mengchen Liu +1
cs.LGstat.MLarXiv:1702.01226v12017
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes +2
cs.CRcs.LGstat.MLarXiv:1904.01067v22019
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham, Michael Milham, Adriana Di Martino +4
stat.MLq-bio.NCarXiv:1611.06066v12016
Mean Absolute Percentage Error for regression models
Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand +1
stat.MLarXiv:1605.02541v22016
The Temple University Hospital Seizure Detection Corpus
Vinit Shah, Eva von Weltin, Silvia Lopez +5
q-bio.QMeess.SPq-bio.NCarXiv:1801.08085v12018
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan +2
cs.LGcs.CVstat.MLarXiv:1902.03680v32019
Wavesplit: End-to-End Speech Separation by Speaker Clustering
Neil Zeghidour, David Grangier
eess.AScs.CLcs.LGarXiv:2002.08933v22020
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi +2
cs.LGstat.MLarXiv:1809.02925v22018
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks
Szu-Wei Fu, Tao-Wei Wang, Yu Tsao +2
stat.MLcs.LGcs.SDarXiv:1709.03658v22017
Motion Planning Networks
Ahmed H. Qureshi, Anthony Simeonov, Mayur J. Bency +1
cs.ROcs.AIstat.MLarXiv:1806.05767v22018
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Bo Chang, Lili Meng, Eldad Haber +3
cs.CVstat.MLarXiv:1709.03698v22017
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang +2
cs.LGcs.AIcs.CVarXiv:1902.03932v22019
Privacy Aware Learning
John C. Duchi, Michael I. Jordan, Martin J. Wainwright
stat.MLcs.ITcs.LGarXiv:1210.2085v22012
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo +3
stat.MLcs.LGarXiv:1711.06178v12017
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
Huaibo Huang, Zhihang Li, Ran He +2
cs.LGcs.CVcs.GRarXiv:1807.06358v22018
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt +3
stat.MLcs.AIcs.LGarXiv:1806.04854v32018
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Di Wang, Minwei Ye, Jinhui Xu
cs.LGcs.CRstat.MLarXiv:1802.05251v12018
Log-concave sampling: Metropolis-Hastings algorithms are fast
Raaz Dwivedi, Yuansi Chen, Martin J. Wainwright +1
stat.MLstat.COarXiv:1801.02309v42018
Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost
Chen Wang, Chengyuan Deng, Suzhen Wang
cs.LGstat.MLarXiv:1908.01672v22019
sktime: A Unified Interface for Machine Learning with Time Series
Markus Löning, Anthony Bagnall, Sajaysurya Ganesh +3
cs.LGstat.MLarXiv:1909.07872v12019
Less is More: Nyström Computational Regularization
Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
stat.MLcs.LGarXiv:1507.04717v62015
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Jacques Wainer, Gavin Cawley
cs.LGstat.MLarXiv:1809.09446v12018
Revisiting Self-Training for Neural Sequence Generation
Junxian He, Jiatao Gu, Jiajun Shen +1
cs.LGcs.CLstat.MLarXiv:1909.13788v32019
The Role of ImageNet Classes in Fréchet Inception Distance
Tuomas Kynkäänniemi, Tero Karras, Miika Aittala +2
cs.CVcs.AIcs.LGarXiv:2203.06026v32022
Mobile Sensor Data Anonymization
Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro +1
cs.LGstat.MLarXiv:1810.11546v32018
Trajectory balance: Improved credit assignment in GFlowNets
Nikolay Malkin, Moksh Jain, Emmanuel Bengio +2
cs.LGstat.MLarXiv:2201.13259v32022
Compositional Vector Space Models for Knowledge Base Completion
Arvind Neelakantan, Benjamin Roth, Andrew McCallum
cs.CLstat.MLarXiv:1504.06662v22015
Pareto Smoothed Importance Sampling
Aki Vehtari, Daniel Simpson, Andrew Gelman +2
stat.COstat.MEstat.MLarXiv:1507.02646v92015
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays
Sivaramakrishnan Rajaraman, Jen Siegelman, Philip O. Alderson +3
eess.IVcs.CVcs.LGarXiv:2004.08379v32020
Few-Shot Learning via Learning the Representation, Provably
Simon S. Du, Wei Hu, Sham M. Kakade +2
cs.LGmath.OCstat.MLarXiv:2002.09434v22020
From Variational to Deterministic Autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari +2
cs.LGstat.MLarXiv:1903.12436v42019
On the Practical Computational Power of Finite Precision RNNs for Language Recognition
Gail Weiss, Yoav Goldberg, Eran Yahav
cs.LGcs.CLstat.MLarXiv:1805.04908v12018
Deep Learning of Subsurface Flow via Theory-guided Neural Network
Nanzhe Wang, Dongxiao Zhang, Haibin Chang +1
cs.LGstat.MLarXiv:1911.00103v12019
An elementary introduction to information geometry
Frank Nielsen
cs.LGcs.ITstat.MLarXiv:1808.08271v22018
Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality
Sai Li, T. Tony Cai, Hongzhe Li
stat.MEstat.MLarXiv:2006.10593v12020
Non-convex Robust PCA
Praneeth Netrapalli, U N Niranjan, Sujay Sanghavi +2
cs.ITcs.LGstat.MLarXiv:1410.7660v12014
Compositional Fairness Constraints for Graph Embeddings
Avishek Joey Bose, William L. Hamilton
cs.LGcs.AIstat.MLarXiv:1905.10674v42019
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression
Jun Qi, Jun Du, Sabato Marco Siniscalchi +2
eess.AScs.LGcs.SDarXiv:2008.07281v12020
ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks
Mahdi Ahmadi, Alireza Norouzi, S. M. Reza Soroushmehr +4
cs.MMcs.CRcs.LGarXiv:1810.07248v32018
Signal Recovery on Graphs: Variation Minimization
Siheng Chen, Aliaksei Sandryhaila, José M. F. Moura +1
cs.SIcs.LGstat.MLarXiv:1411.7414v32014
Lyapunov-based Safe Policy Optimization for Continuous Control
Yinlam Chow, Ofir Nachum, Aleksandra Faust +2
cs.LGcs.AIstat.MLarXiv:1901.10031v22019
Learning to Self-Train for Semi-Supervised Few-Shot Classification
Xinzhe Li, Qianru Sun, Yaoyao Liu +4
cs.CVcs.LGstat.MLarXiv:1906.00562v22019
On Markov chain Monte Carlo methods for tall data
Rémi Bardenet, Arnaud Doucet, Chris Holmes
stat.MEstat.COstat.MLarXiv:1505.02827v12015
Challenges and Opportunities in Quantum Machine Learning
M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang +2
quant-phcs.LGstat.MLarXiv:2303.09491v12023
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Michela Paganini, Luke de Oliveira, Benjamin Nachman
hep-excs.LGhep-pharXiv:1712.10321v12017
The ground truth about metadata and community detection in networks
Leto Peel, Daniel B. Larremore, Aaron Clauset
cs.SIphysics.data-anphysics.soc-pharXiv:1608.05878v22016
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
Dongkun Zhang, Ling Guo, George Em Karniadakis
cs.LGmath.NAphysics.comp-pharXiv:1905.01205v22019
Neural Logic Machines
Honghua Dong, Jiayuan Mao, Tian Lin +3
cs.AIcs.LGstat.MLarXiv:1904.11694v12019
Adaptive Graph Encoder for Attributed Graph Embedding
Ganqu Cui, Jie Zhou, Cheng Yang +1
cs.LGstat.MLarXiv:2007.01594v12020
Everything is Connected: Graph Neural Networks
Petar Veličković
cs.LGcs.AIcs.SIarXiv:2301.08210v12023
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Sung Whan Yoon, Jun Seo, Jaekyun Moon
cs.LGstat.MLarXiv:1905.06549v22019

Machine Learning (stat)

Distributed learning of deep neural network over multiple agents

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

Handling Incomplete Heterogeneous Data using VAEs

Confounding variables can degrade generalization performance of radiological deep learning models

Deep learning in business analytics and operations research: Models, applications and managerial implications

Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data

Robust Loss Functions under Label Noise for Deep Neural Networks

Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations

Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective

Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

Mean Absolute Percentage Error for regression models

The Temple University Hospital Seizure Detection Corpus

Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

Wavesplit: End-to-End Speech Separation by Speaker Clustering

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks

Motion Planning Networks

Reversible Architectures for Arbitrarily Deep Residual Neural Networks

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

Privacy Aware Learning

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Differentially Private Empirical Risk Minimization Revisited: Faster and More General

Log-concave sampling: Metropolis-Hastings algorithms are fast

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost

sktime: A Unified Interface for Machine Learning with Time Series

Less is More: Nyström Computational Regularization

Nested cross-validation when selecting classifiers is overzealous for most practical applications

Revisiting Self-Training for Neural Sequence Generation

The Role of ImageNet Classes in Fréchet Inception Distance

Mobile Sensor Data Anonymization

Trajectory balance: Improved credit assignment in GFlowNets

Compositional Vector Space Models for Knowledge Base Completion

Pareto Smoothed Importance Sampling

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays

Few-Shot Learning via Learning the Representation, Provably

From Variational to Deterministic Autoencoders

On the Practical Computational Power of Finite Precision RNNs for Language Recognition

Deep Learning of Subsurface Flow via Theory-guided Neural Network

An elementary introduction to information geometry

Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality

Non-convex Robust PCA

Compositional Fairness Constraints for Graph Embeddings

On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks

Signal Recovery on Graphs: Variation Minimization

Lyapunov-based Safe Policy Optimization for Continuous Control

Learning to Self-Train for Semi-Supervised Few-Shot Classification

On Markov chain Monte Carlo methods for tall data

Challenges and Opportunities in Quantum Machine Learning

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

The ground truth about metadata and community detection in networks

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

Neural Logic Machines

Adaptive Graph Encoder for Attributed Graph Embedding

Everything is Connected: Graph Neural Networks

TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning