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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8,281 to 8,340 of 9,690

Simplex Diffusion Models
Justin Deschenaux, Alexandre Galashov, Andrew Campbell +4
cs.LGstat.MLarXiv:2609.35553v12026
An Efficient Framework for Clustered Federated Learning
Avishek Ghosh, Jichan Chung, Dong Yin +1
stat.MLcs.LGarXiv:2006.04088v22020
DrNAS: Dirichlet Neural Architecture Search
Xiangning Chen, Ruochen Wang, Minhao Cheng +2
cs.LGcs.CVstat.MLarXiv:2006.10355v42020
Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks
Xiaoyu Li, Zhizhou Sha, Jiaojiao Jiang +2
cs.LGstat.MLarXiv:2609.09130v12026
MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies
Yair Weiss, Chen Yanover, Talya Meltzer
cs.AIcs.LGstat.MLarXiv:1206.5286v12012
EmbraceNet: A robust deep learning architecture for multimodal classification
Jun-Ho Choi, Jong-Seok Lee
cs.LGstat.MLarXiv:1904.09078v12019
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Chi Jin, Praneeth Netrapalli, Rong Ge +2
cs.LGmath.OCstat.MLarXiv:1902.04811v22019
High-dimensional regression adjustments in randomized experiments
Stefan Wager, Wenfei Du, Jonathan Taylor +1
stat.MEstat.MLarXiv:1607.06801v32016
A Subsampled Davis-Kahan Bound for Large-Scale Eigenspace Estimation
Huan Qing
stat.MLcs.LGstat.MEarXiv:2609.09211v12026
Truncated Back-propagation for Bilevel Optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch +1
cs.LGstat.MLarXiv:1810.10667v22018
Safe Policy Improvement by Minimizing Robust Baseline Regret
Marek Petrik, Yinlam Chow, Mohammad Ghavamzadeh
stat.MLarXiv:1607.03842v12016
A Constructive Prediction of the Generalization Error Across Scales
Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov +1
cs.LGcs.CLcs.CVarXiv:1909.12673v22019
Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming
Yangyang Xu
math.OCcs.NAmath.NAarXiv:1606.09155v12016
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
Namyong Park, Andrey Kan, Xin Luna Dong +2
cs.LGcs.IRstat.MLarXiv:1905.08865v22019
The multi-armed bandit problem with covariates
Vianney Perchet, Philippe Rigollet
math.STcs.LGstat.MLarXiv:1110.6084v32011
Efficient Private ERM for Smooth Objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou +1
cs.LGcs.DSstat.MLarXiv:1703.09947v22017
A Consistent and Differentiable Lp Canonical Calibration Error Estimator
Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko
stat.MLcs.CVarXiv:2210.07810v12022
Variational Dropout Sparsifies Deep Neural Networks
Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
stat.MLcs.LGarXiv:1701.05369v32017
The Rate of Convergence of AdaBoost
Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire
math.OCcs.AIstat.MLarXiv:1106.6024v12011
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Axel Abels, Diederik M. Roijers, Tom Lenaerts +2
cs.LGcs.AIstat.MLarXiv:1809.07803v22018
Online Learning Rate Adaptation with Hypergradient Descent
Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio +2
cs.LGstat.MLarXiv:1703.04782v32017
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
Julien Mairal
stat.MLcs.CVcs.LGarXiv:1605.06265v22016
cGANs with Projection Discriminator
Takeru Miyato, Masanori Koyama
cs.LGcs.CVstat.MLarXiv:1802.05637v22018
A Bayesian Perspective on the Deep Image Prior
Zezhou Cheng, Matheus Gadelha, Subhransu Maji +1
cs.CVcs.LGstat.MLarXiv:1904.07457v12019
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, Jure Leskovec
cs.SIcs.LGstat.MLarXiv:1706.02216v42017
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
Yuqing Hu, Vincent Gripon, Stéphane Pateux
cs.LGstat.MLarXiv:2006.03806v32020
On the marginal likelihood and cross-validation
Edwin Fong, Chris Holmes
stat.MEstat.MLarXiv:1905.08737v22019
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He +7
cs.LGcs.AIcs.ROarXiv:1910.10897v22019
DKN: Deep Knowledge-Aware Network for News Recommendation
Hongwei Wang, Fuzheng Zhang, Xing Xie +1
stat.MLcs.LGarXiv:1801.08284v22018
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
Ting Chen, Yizhou Sun
cs.LGcs.AIcs.IRarXiv:1612.02814v22016
Expert Gate: Lifelong Learning with a Network of Experts
Rahaf Aljundi, Punarjay Chakravarty, Tinne Tuytelaars
cs.CVcs.AIstat.MLarXiv:1611.06194v22016
Scalable and Sustainable Deep Learning via Randomized Hashing
Ryan Spring, Anshumali Shrivastava
stat.MLcs.LGcs.NEarXiv:1602.08194v22016
Towards a universal neural network encoder for time series
Joan Serrà, Santiago Pascual, Alexandros Karatzoglou
cs.LGcs.NEstat.MLarXiv:1805.03908v12018
Optimal estimation for Functional Linear Regression with Noisy Discretized Data
Sixtine Sphabmixay
stat.MLcs.LGarXiv:2609.08671v12026
Large-Scale Learnable Graph Convolutional Networks
Hongyang Gao, Zhengyang Wang, Shuiwang Ji
cs.LGstat.MLarXiv:1808.03965v12018
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
Erik Englesson, Hossein Azizpour
cs.LGcs.CVstat.MLarXiv:2105.04522v42021
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm
Prateek Jain, Chi Jin, Sham M. Kakade +2
cs.LGcs.DScs.NEarXiv:1602.06929v22016
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Bo Chang, Lili Meng, Eldad Haber +3
cs.CVstat.MLarXiv:1709.03698v22017
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
Colin Wei, Sang Michael Xie, Tengyu Ma
cs.LGstat.MLarXiv:2106.09226v22021
Instability in Patient Clustering: A Multiverse Analysis of Unsupervised Clustering in the CENTER-TBI cohort
Sean R E A Bagcik, Aneeta Merlin Chacko, Ewout W Steyerberg +4
stat.MEstat.APstat.MLarXiv:2609.08577v12026
An improvement of the convergence proof of the ADAM-Optimizer
Sebastian Bock, Josef Goppold, Martin Weiß
cs.LGcs.AIstat.MLarXiv:1804.10587v12018
State-Only Imitation Learning for Dexterous Manipulation
Ilija Radosavovic, Xiaolong Wang, Lerrel Pinto +1
cs.ROcs.LGstat.MLarXiv:2004.04650v22020
A Review of Feature Selection Methods Based on Mutual Information
Jorge R. Vergara, Pablo A. Estévez
cs.LGstat.MLarXiv:1509.07577v12015
Time-Varying Gaussian Process Bandit Optimization
Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher
stat.MLcs.LGarXiv:1601.06650v12016
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich +1
cs.LGmath.OCstat.MLarXiv:1901.09847v22019
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Shaoxing Mo, Yinhao Zhu, Nicholas Zabaras +2
stat.MLcs.LGarXiv:1807.00882v12018
Interpretable and Steerable Sequence Learning via Prototypes
Yao Ming, Panpan Xu, Huamin Qu +1
cs.LGcs.HCstat.MLarXiv:1907.09728v12019
Intensity-Free Learning of Temporal Point Processes
Oleksandr Shchur, Marin Biloš, Stephan Günnemann
cs.LGstat.MLarXiv:1909.12127v22019
Crime prediction through urban metrics and statistical learning
Luiz G A Alves, Haroldo V Ribeiro, Francisco A Rodrigues
physics.soc-phstat.APstat.MLarXiv:1712.03834v22017
Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi +1
cs.LGstat.MLarXiv:1705.10257v22017
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Zixiang Chen, Yihe Deng, Huizhuo Yuan +2
cs.LGcs.AIcs.CLarXiv:2401.01335v32024
Variational Auto-encoded Deep Gaussian Processes
Zhenwen Dai, Andreas Damianou, Javier González +1
cs.LGstat.MLarXiv:1511.06455v22015
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
Jiaxi Tang, Ke Wang
cs.LGcs.IRstat.MLarXiv:1809.07428v12018
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels
Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
math.STcs.LGstat.MLarXiv:1908.10292v22019
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Alex Sherstinsky
cs.LGstat.MLarXiv:1808.03314v102018
Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions
Xiaodong Li
cs.ITstat.MLarXiv:1104.1041v22011
Deep MIMO Detection
Neev Samuel, Tzvi Diskin, Ami Wiesel
stat.MLcs.ITcs.LGarXiv:1706.01151v12017
One-Shot Generalization in Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka +2
stat.MLcs.AIcs.LGarXiv:1603.05106v22016
Universal low-rank matrix recovery from Pauli measurements
Yi-Kai Liu
quant-phcs.ITmath.ITarXiv:1103.2816v22011
Variational Fourier features for Gaussian processes
James Hensman, Nicolas Durrande, Arno Solin
stat.MLarXiv:1611.06740v22016

Machine Learning (stat)

Simplex Diffusion Models

An Efficient Framework for Clustered Federated Learning

DrNAS: Dirichlet Neural Architecture Search

Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks

MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies

EmbraceNet: A robust deep learning architecture for multimodal classification

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points

High-dimensional regression adjustments in randomized experiments

A Subsampled Davis-Kahan Bound for Large-Scale Eigenspace Estimation

Truncated Back-propagation for Bilevel Optimization

Safe Policy Improvement by Minimizing Robust Baseline Regret

A Constructive Prediction of the Generalization Error Across Scales

Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

The multi-armed bandit problem with covariates

Efficient Private ERM for Smooth Objectives

A Consistent and Differentiable Lp Canonical Calibration Error Estimator

Variational Dropout Sparsifies Deep Neural Networks

The Rate of Convergence of AdaBoost

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

Online Learning Rate Adaptation with Hypergradient Descent

End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

cGANs with Projection Discriminator

A Bayesian Perspective on the Deep Image Prior

Inductive Representation Learning on Large Graphs

Leveraging the Feature Distribution in Transfer-based Few-Shot Learning

On the marginal likelihood and cross-validation

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

DKN: Deep Knowledge-Aware Network for News Recommendation

Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification

Expert Gate: Lifelong Learning with a Network of Experts

Scalable and Sustainable Deep Learning via Randomized Hashing

Towards a universal neural network encoder for time series

Optimal estimation for Functional Linear Regression with Noisy Discretized Data

Large-Scale Learnable Graph Convolutional Networks

Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm

Reversible Architectures for Arbitrarily Deep Residual Neural Networks

Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning

Instability in Patient Clustering: A Multiverse Analysis of Unsupervised Clustering in the CENTER-TBI cohort

An improvement of the convergence proof of the ADAM-Optimizer

State-Only Imitation Learning for Dexterous Manipulation

A Review of Feature Selection Methods Based on Mutual Information

Time-Varying Gaussian Process Bandit Optimization

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

Interpretable and Steerable Sequence Learning via Prototypes

Intensity-Free Learning of Temporal Point Processes

Crime prediction through urban metrics and statistical learning

Boltzmann Exploration Done Right

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Variational Auto-encoded Deep Gaussian Processes

Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network

Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions

Deep MIMO Detection

One-Shot Generalization in Deep Generative Models

Universal low-rank matrix recovery from Pauli measurements

Variational Fourier features for Gaussian processes