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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2,941 to 3,000 of 6,790

Intensity-Free Learning of Temporal Point Processes
Oleksandr Shchur, Marin Biloš, Stephan Günnemann
cs.LGstat.MLarXiv:1909.12127v22019
Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform
Zhenyu Zhao, Radhika Anand, Mallory Wang
stat.MLcs.LGarXiv:1908.05376v12019
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Adnan Qayyum, Muhammad Usama, Junaid Qadir +1
cs.LGcs.CRstat.MLarXiv:1905.12762v12019
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks
Jen-Cheng Hou, Syu-Siang Wang, Ying-Hui Lai +3
cs.SDcs.MMeess.ASarXiv:1709.00944v52017
Diversity-Sensitive Conditional Generative Adversarial Networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang +2
cs.LGstat.MLarXiv:1901.09024v12019
What Do Compressed Deep Neural Networks Forget?
Sara Hooker, Aaron Courville, Gregory Clark +2
cs.LGcs.AIcs.CVarXiv:1911.05248v32019
A General Algorithm for Deciding Transportability of Experimental Results
Elias Bareinboim, Judea Pearl
cs.AIstat.MEstat.MLarXiv:1312.7485v12013
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Michael Roberts, Derek Driggs, Matthew Thorpe +13
cs.LGcs.CVeess.IVarXiv:2008.06388v42020
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
Chong You, Daniel P. Robinson, Rene Vidal
cs.CVcs.LGstat.MLarXiv:1507.01238v32015
Between-class Learning for Image Classification
Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada
cs.LGcs.CVstat.MLarXiv:1711.10284v22017
Explainable artificial intelligence model to predict acute critical illness from electronic health records
Simon Meyer Lauritsen, Mads Kristensen, Mathias Vassard Olsen +5
cs.AIcs.LGstat.AParXiv:1912.01266v12019
Truly Proximal Policy Optimization
Yuhui Wang, Hao He, Chao Wen +1
cs.LGcs.AIstat.MLarXiv:1903.07940v22019
Veridical Data Science
Bin Yu, Karl Kumbier
stat.MLcs.LGarXiv:1901.08152v52019
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Tengfei Ma, Jie Chen, Cao Xiao
cs.LGstat.MLarXiv:1809.02630v22018
Higher-Order Factorization Machines
Mathieu Blondel, Akinori Fujino, Naonori Ueda +1
stat.MLcs.LGarXiv:1607.07195v22016
Deep Learning Approximation for Stochastic Control Problems
Jiequn Han, Weinan E
cs.LGcs.AIcs.NEarXiv:1611.07422v12016
Sentence-State LSTM for Text Representation
Yue Zhang, Qi Liu, Linfeng Song
cs.CLcs.LGstat.MLarXiv:1805.02474v12018
On Valid Optimal Assignment Kernels and Applications to Graph Classification
Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson
cs.LGstat.MLarXiv:1606.01141v32016
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu +1
cs.LGstat.MLarXiv:2110.03753v32021
A cost function for similarity-based hierarchical clustering
Sanjoy Dasgupta
cs.DScs.LGstat.MLarXiv:1510.05043v12015
An Introduction to Probabilistic Programming
Jan-Willem van de Meent, Brooks Paige, Hongseok Yang +1
stat.MLcs.AIcs.LGarXiv:1809.10756v22018
Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models
Giorgio Visani, Enrico Bagli, Federico Chesani +2
cs.LGcs.AIstat.MLarXiv:2001.11757v22020
Criteria for Classifying Forecasting Methods
Tim Januschowski, Jan Gasthaus, Yuyang Wang +4
stat.MLcs.LGarXiv:2212.03523v12022
Implicit Deep Learning
Laurent El Ghaoui, Fangda Gu, Bertrand Travacca +2
cs.LGmath.OCstat.MLarXiv:1908.06315v42019
Safe Policy Improvement with Baseline Bootstrapping
Romain Laroche, Paul Trichelair, Rémi Tachet des Combes
cs.LGcs.AIstat.MLarXiv:1712.06924v52017
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas +1
cs.LGcs.DCmath.OCarXiv:1910.00643v22019
Defensive Quantization: When Efficiency Meets Robustness
Ji Lin, Chuang Gan, Song Han
cs.LGcs.CVstat.MLarXiv:1904.08444v12019
Stochastic Normalizing Flows
Hao Wu, Jonas Köhler, Frank Noé
stat.MLcs.LGphysics.chem-pharXiv:2002.06707v32020
Collaborative Fairness in Federated Learning
Lingjuan Lyu, Xinyi Xu, Qian Wang
cs.LGcs.DCstat.MLarXiv:2008.12161v22020
Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
Ahmed Zamzam, Kyri Baker
cs.LGeess.SPeess.SYarXiv:1910.01213v12019
Communication Complexity of Distributed Convex Learning and Optimization
Yossi Arjevani, Ohad Shamir
cs.LGmath.OCstat.MLarXiv:1506.01900v22015
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
Tianzhe Wang, Kuan Wang, Han Cai +3
cs.LGcs.CVstat.MLarXiv:2006.08509v12020
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin +2
cs.LGstat.MLarXiv:1805.04276v22018
Making brain-machine interfaces robust to future neural variability
David Sussillo, Sergey D. Stavisky, Jonathan C. Kao +2
q-bio.NCstat.MLarXiv:1610.05872v12016
Summaries:한국어
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk +2
cs.LGstat.MLarXiv:1907.09615v12019
Compositional Explanations of Neurons
Jesse Mu, Jacob Andreas
cs.LGcs.AIcs.CLarXiv:2006.14032v22020
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Geoffrey Roeder, Yuhuai Wu, David Duvenaud
stat.MLcs.LGarXiv:1703.09194v32017
Convergence Rate of Frank-Wolfe for Non-Convex Objectives
Simon Lacoste-Julien
math.OCcs.LGmath.NAarXiv:1607.00345v12016
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
Florian Ziel, Rafal Weron
stat.APq-fin.STstat.MLarXiv:1805.06649v12018
Generative Adversarial Network for Medical Images (MI-GAN)
Talha Iqbal, Hazrat Ali
cs.LGcs.CVeess.IVarXiv:1810.00551v12018
A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander J. Ratner +3
cs.LGstat.MLarXiv:1803.06084v22018
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong, Frank R. Schmidt, J. Zico Kolter
cs.LGstat.MLarXiv:1902.07906v22019
Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware
Peter Blouw, Xuan Choo, Eric Hunsberger +1
cs.LGstat.MLarXiv:1812.01739v22018
Understanding the Origins of Bias in Word Embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson +1
cs.LGcs.CYstat.MLarXiv:1810.03611v22018
Barnes-Hut-SNE
Laurens van der Maaten
cs.LGcs.CVstat.MLarXiv:1301.3342v22013
Generalization and Regularization in DQN
Jesse Farebrother, Marlos C. Machado, Michael Bowling
cs.LGcs.AIstat.MLarXiv:1810.00123v32018
Boosting Few-Shot Learning With Adaptive Margin Loss
Aoxue Li, Weiran Huang, Xu Lan +3
cs.CVcs.LGstat.MLarXiv:2005.13826v12020
From Parity to Preference-based Notions of Fairness in Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez +2
stat.MLcs.LGarXiv:1707.00010v22017
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li +3
cs.LGcs.IRstat.MLarXiv:1812.10613v32018
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models
Tiago P. Peixoto
physics.data-ancond-mat.stat-mechcs.SIarXiv:1310.4378v32013
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Chunyuan Li, Hao Liu, Changyou Chen +4
stat.MLcs.AIcs.CVarXiv:1709.01215v22017
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
Guineng Zheng, Subhabrata Mukherjee, Xin Luna Dong +1
cs.CLcs.AIcs.IRarXiv:1806.01264v22018
Model Agnostic Supervised Local Explanations
Gregory Plumb, Denali Molitor, Ameet Talwalkar
cs.LGstat.MLarXiv:1807.02910v32018
Texture Synthesis with Spatial Generative Adversarial Networks
Nikolay Jetchev, Urs Bergmann, Roland Vollgraf
cs.CVstat.MLarXiv:1611.08207v42016
A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding +1
cs.IRcs.LGstat.MLarXiv:1605.09477v12016
BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems
Jiawei Shao, Jun Zhang
cs.LGeess.SPstat.MLarXiv:1910.14315v52019
Exploring Adversarial Examples in Malware Detection
Octavian Suciu, Scott E. Coull, Jeffrey Johns
cs.LGcs.CRstat.MLarXiv:1810.08280v32018
Multiplicative LSTM for sequence modelling
Ben Krause, Liang Lu, Iain Murray +1
cs.NEstat.MLarXiv:1609.07959v32016
To be Robust or to be Fair: Towards Fairness in Adversarial Training
Han Xu, Xiaorui Liu, Yaxin Li +2
cs.LGstat.MLarXiv:2010.06121v22020
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models
Cassidy Laidlaw, Sahil Singla, Soheil Feizi
cs.LGcs.CVstat.MLarXiv:2006.12655v42020

Machine Learning (stat)

Intensity-Free Learning of Temporal Point Processes

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward

Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks

Diversity-Sensitive Conditional Generative Adversarial Networks

What Do Compressed Deep Neural Networks Forget?

A General Algorithm for Deciding Transportability of Experimental Results

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit

Between-class Learning for Image Classification

Explainable artificial intelligence model to predict acute critical illness from electronic health records

Truly Proximal Policy Optimization

Veridical Data Science

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

Higher-Order Factorization Machines

Deep Learning Approximation for Stochastic Control Problems

Sentence-State LSTM for Text Representation

On Valid Optimal Assignment Kernels and Applications to Graph Classification

From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

A cost function for similarity-based hierarchical clustering

An Introduction to Probabilistic Programming

Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models

Criteria for Classifying Forecasting Methods

Implicit Deep Learning

Safe Policy Improvement with Baseline Bootstrapping

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

Defensive Quantization: When Efficiency Meets Robustness

Stochastic Normalizing Flows

Collaborative Fairness in Federated Learning

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

Communication Complexity of Distributed Convex Learning and Optimization

APQ: Joint Search for Network Architecture, Pruning and Quantization Policy

Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis

Making brain-machine interfaces robust to future neural variability

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Compositional Explanations of Neurons

Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

Convergence Rate of Frank-Wolfe for Non-Convex Objectives

Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

Generative Adversarial Network for Medical Images (MI-GAN)

A Kernel Theory of Modern Data Augmentation

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations

Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware

Understanding the Origins of Bias in Word Embeddings

Barnes-Hut-SNE

Generalization and Regularization in DQN

Boosting Few-Shot Learning With Adaptive Margin Loss

From Parity to Preference-based Notions of Fairness in Classification

Generative Adversarial User Model for Reinforcement Learning Based Recommendation System

Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]

Model Agnostic Supervised Local Explanations

Texture Synthesis with Spatial Generative Adversarial Networks

A Neural Autoregressive Approach to Collaborative Filtering

BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems

Exploring Adversarial Examples in Malware Detection

Multiplicative LSTM for sequence modelling

To be Robust or to be Fair: Towards Fairness in Adversarial Training

Perceptual Adversarial Robustness: Defense Against Unseen Threat Models