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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181 to 240 of 6,785

Sparse Subspace Clustering: Algorithm, Theory, and Applications
Ehsan Elhamifar, Rene Vidal
cs.CVcs.IRcs.ITarXiv:1203.1005v32012
Generalized Fisher Score for Feature Selection
Quanquan Gu, Zhenhui Li, Jiawei Han
cs.LGstat.MLarXiv:1202.3725v12012
Practical Lossless Compression with Latent Variables using Bits Back Coding
James Townsend, Tom Bird, David Barber
cs.LGcs.AIcs.ITarXiv:1901.04866v12019
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
Aditya Kusupati, Manish Singh, Kush Bhatia +3
cs.LGcs.AIcs.NEarXiv:1901.02358v12019
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Shengding Hu +6
cs.LGcs.AIstat.MLarXiv:1812.08434v62018
Statistical Topic Models for Multi-Label Document Classification
Timothy N. Rubin, America Chambers, Padhraic Smyth +1
stat.MLcs.LGarXiv:1107.2462v22011
Improving Robustness using Generated Data
Sven Gowal, Sylvestre-Alvise Rebuffi, Olivia Wiles +3
cs.LGcs.CVstat.MLarXiv:2110.09468v22021
Self-supervised Learning is More Robust to Dataset Imbalance
Hong Liu, Jeff Z. HaoChen, Adrien Gaidon +1
cs.LGcs.CVstat.MLarXiv:2110.05025v22021
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
Titouan Vayer, Laetita Chapel, Rémi Flamary +2
stat.MLcs.LGarXiv:1811.02834v12018
GANs for Medical Image Analysis
Salome Kazeminia, Christoph Baur, Arjan Kuijper +4
cs.CVcs.LGstat.MLarXiv:1809.06222v32018
Don't Use Large Mini-Batches, Use Local SGD
Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel +1
cs.LGstat.MLarXiv:1808.07217v62018
Summaries:한국어
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
Mostafa Karimi, Di Wu, Zhangyang Wang +1
q-bio.BMcs.LGstat.MLarXiv:1806.07537v22018
Neural Code Comprehension: A Learnable Representation of Code Semantics
Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
cs.LGcs.NEcs.PLarXiv:1806.07336v32018
Unsupervised Alignment of Embeddings with Wasserstein Procrustes
Edouard Grave, Armand Joulin, Quentin Berthet
cs.LGcs.CLstat.MLarXiv:1805.11222v12018
A tutorial on conformal prediction
Glenn Shafer, Vladimir Vovk
cs.LGstat.MLarXiv:0706.3188v12007
Sample Complexity of Multi-task Reinforcement Learning
Emma Brunskill, Lihong Li
cs.LGstat.MLarXiv:1309.6821v12013
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
Shashank Rajput, Hongyi Wang, Zachary Charles +1
cs.LGcs.DCstat.MLarXiv:1907.12205v22019
Autoregressive Entity Retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel +1
cs.CLcs.IRcs.LGarXiv:2010.00904v32020
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
Xiangxiang Zeng, Xiang Song, Tengfei Ma +6
q-bio.QMcs.LGstat.MLarXiv:2005.10831v12020
An Evaluation of Change Point Detection Algorithms
Gerrit J. J. van den Burg, Christopher K. I. Williams
stat.MLcs.LGstat.MEarXiv:2003.06222v32020
Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales
Dapeng Feng, Kuai Fang, Chaopeng Shen
cs.LGstat.MLarXiv:1912.08949v32019
Macro F1 and Macro F1
Juri Opitz, Sebastian Burst
cs.LGstat.MLarXiv:1911.03347v32019
Non-linear regression models for Approximate Bayesian Computation
M. G. B. Blum, O. Francois
stat.COstat.MLarXiv:0809.4178v22008
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk +1
cs.LGcs.PLstat.MLarXiv:1812.03973v32018
Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Ryoma Sato, Makoto Yamada, Hisashi Kashima
cs.LGstat.MLarXiv:1905.10261v22019
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Jinmian Ye, Linnan Wang, Guangxi Li +4
cs.LGstat.MLarXiv:1712.05134v22017
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
Ehsan Amid, Manfred K. Warmuth, Rohan Anil +1
cs.LGstat.MLarXiv:1906.03361v32019
COVID-19 diagnosis by routine blood tests using machine learning
Matjaž Kukar, Gregor Gunčar, Tomaž Vovko +7
physics.med-phcs.LGq-bio.QMarXiv:2006.03476v12020
Single-stream Policy Optimization
Zhongwen Xu, Zihan Ding
cs.LGcs.AIstat.MLarXiv:2509.13232v22025
Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks
Ahmad Halimi Razlighi, Mohammad Siddiqur Rahman, Maximilian H. V. Tillmann +2
eess.SPcs.LGeess.IVarXiv:2609.03977v12026
Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling
Huangjie Zheng, Shansan Gong, Ruixiang Zhang +5
stat.MLcs.LGarXiv:2510.01329v12025
Improving the Expected Improvement Algorithm
Chao Qin, Diego Klabjan, Daniel Russo
cs.LGstat.MLarXiv:1705.10033v12017
The Fast Convergence of Incremental PCA
Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
cs.LGstat.MLarXiv:1501.03796v12015
Dual Supervised Learning
Yingce Xia, Tao Qin, Wei Chen +3
cs.LGstat.MLarXiv:1707.00415v12017
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks
Mert Pilanci, Tolga Ergen
cs.LGcs.CCstat.MLarXiv:2002.10553v22020
The Information Geometry of Mirror Descent
Garvesh Raskutti, Sayan Mukherjee
stat.MLcs.LGarXiv:1310.7780v22013
COCO-GAN: Generation by Parts via Conditional Coordinating
Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen +3
cs.LGcs.CVstat.MLarXiv:1904.00284v42019
Practical Contextual Bandits with Regression Oracles
Dylan J. Foster, Alekh Agarwal, Miroslav Dudík +2
cs.LGstat.MLarXiv:1803.01088v12018
Distinguishing cause from effect using observational data: methods and benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing +2
cs.LGcs.AIstat.MLarXiv:1412.3773v32014
On the Pitfalls of Measuring Emergent Communication
Ryan Lowe, Jakob Foerster, Y-Lan Boureau +2
cs.LGcs.AIcs.CLarXiv:1903.05168v12019
Correlational Neural Networks
Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle +1
cs.CLcs.LGcs.NEarXiv:1504.07225v32015
VC Classes are Adversarially Robustly Learnable, but Only Improperly
Omar Montasser, Steve Hanneke, Nathan Srebro
cs.LGstat.MLarXiv:1902.04217v22019
A theory of multiclass boosting
Indraneel Mukherjee, Robert E. Schapire
stat.MLcs.AIarXiv:1108.2989v12011
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Seungyong Moon, Gaon An, Hyun Oh Song
cs.LGcs.CRcs.CVarXiv:1905.06635v22019
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas +3
stat.MLcs.LGarXiv:1807.05031v62018
Semi-Implicit Variational Inference
Mingzhang Yin, Mingyuan Zhou
stat.MLcs.LGstat.COarXiv:1805.11183v12018
Defending Against Physically Realizable Attacks on Image Classification
Tong Wu, Liang Tong, Yevgeniy Vorobeychik
cs.LGcs.AIcs.CVarXiv:1909.09552v22019
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
Andrea Zanette, David Brandfonbrener, Emma Brunskill +2
cs.LGstat.MLarXiv:1911.00567v72019
Learning Latent Space Energy-Based Prior Model
Bo Pang, Tian Han, Erik Nijkamp +2
stat.MLcs.LGarXiv:2006.08205v22020
An optimal algorithm for the Thresholding Bandit Problem
Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier
stat.MLcs.LGarXiv:1605.08671v12016
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
Shusen Wang, Farbod Roosta-Khorasani, Peng Xu +1
cs.LGcs.DCmath.OCarXiv:1709.03528v52017
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma +1
stat.MLcs.LGarXiv:2002.11537v42020
Online Convex Optimization in Adversarial Markov Decision Processes
Aviv Rosenberg, Yishay Mansour
cs.LGcs.AIstat.MLarXiv:1905.07773v12019
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
Yong Luo, Dacheng Tao, Chang Xu +3
stat.MLcs.CVcs.LGarXiv:1904.03921v12019
Deep Residual Learning in the JPEG Transform Domain
Max Ehrlich, Larry Davis
cs.LGcs.CVstat.MLarXiv:1812.11690v32018
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
Iris A. M. Huijben, Wouter Kool, Max B. Paulus +1
cs.LGstat.MLarXiv:2110.01515v22021
Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems
Thomas Beckers, Dana Kulić, Sandra Hirche
cs.LGeess.SYstat.MLarXiv:1806.07190v22018
Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
Jinghe Zhang, Kamran Kowsari, James H. Harrison +2
q-bio.QMcs.AIcs.IRarXiv:1810.04793v32018
Synthesizing Normalized Faces from Facial Identity Features
Forrester Cole, David Belanger, Dilip Krishnan +3
cs.CVstat.MLarXiv:1701.04851v42017
Decision Trees for Decision-Making under the Predict-then-Optimize Framework
Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis
cs.LGmath.OCstat.MLarXiv:2003.00360v22020

Machine Learning (stat)

Sparse Subspace Clustering: Algorithm, Theory, and Applications

Generalized Fisher Score for Feature Selection

Practical Lossless Compression with Latent Variables using Bits Back Coding

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

Graph Neural Networks: A Review of Methods and Applications

Statistical Topic Models for Multi-Label Document Classification

Improving Robustness using Generated Data

Self-supervised Learning is More Robust to Dataset Imbalance

Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties

GANs for Medical Image Analysis

Don't Use Large Mini-Batches, Use Local SGD

DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks

Neural Code Comprehension: A Learnable Representation of Code Semantics

Unsupervised Alignment of Embeddings with Wasserstein Procrustes

A tutorial on conformal prediction

Sample Complexity of Multi-task Reinforcement Learning

DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation

Autoregressive Entity Retrieval

Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning

An Evaluation of Change Point Detection Algorithms

Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales

Macro F1 and Macro F1

Non-linear regression models for Approximate Bayesian Computation

Bayesian Layers: A Module for Neural Network Uncertainty

Approximation Ratios of Graph Neural Networks for Combinatorial Problems

Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition

Robust Bi-Tempered Logistic Loss Based on Bregman Divergences

COVID-19 diagnosis by routine blood tests using machine learning

Single-stream Policy Optimization

Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks

Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling

Improving the Expected Improvement Algorithm

The Fast Convergence of Incremental PCA

Dual Supervised Learning

Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks

The Information Geometry of Mirror Descent

COCO-GAN: Generation by Parts via Conditional Coordinating

Practical Contextual Bandits with Regression Oracles

Distinguishing cause from effect using observational data: methods and benchmarks

On the Pitfalls of Measuring Emergent Communication

Correlational Neural Networks

VC Classes are Adversarially Robustly Learnable, but Only Improperly

A theory of multiclass boosting

Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length

Semi-Implicit Variational Inference

Defending Against Physically Realizable Attacks on Image Classification

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration

Learning Latent Space Energy-Based Prior Model

An optimal algorithm for the Thresholding Bandit Problem

GIANT: Globally Improved Approximate Newton Method for Distributed Optimization

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA

Online Convex Optimization in Adversarial Markov Decision Processes

Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

Deep Residual Learning in the JPEG Transform Domain

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

Synthesizing Normalized Faces from Facial Identity Features

Decision Trees for Decision-Making under the Predict-then-Optimize Framework