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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3,661 to 3,720 of 6,773

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz +2
cs.LGcs.AIstat.MLarXiv:2104.05522v62021
Defending against Backdoors in Federated Learning with Robust Learning Rate
Mustafa Safa Ozdayi, Murat Kantarcioglu, Yulia R. Gel
cs.LGcs.CRstat.MLarXiv:2007.03767v42020
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
Jason Jo, Yoshua Bengio
cs.LGstat.MLarXiv:1711.11561v12017
Random Feature Maps for Dot Product Kernels
Purushottam Kar, Harish Karnick
cs.LGcs.CGmath.FAarXiv:1201.6530v32012
Implicit Reparameterization Gradients
Michael Figurnov, Shakir Mohamed, Andriy Mnih
cs.LGstat.MLarXiv:1805.08498v42018
Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy, Yair Carmon, John C. Duchi +1
math.OCcs.LGstat.MLarXiv:2010.05893v22020
Learning Likelihoods with Conditional Normalizing Flows
Christina Winkler, Daniel Worrall, Emiel Hoogeboom +1
cs.LGcs.CVstat.MLarXiv:1912.00042v22019
Modeling Missing Data in Clinical Time Series with RNNs
Zachary C. Lipton, David C. Kale, Randall Wetzel
cs.LGcs.IRcs.NEarXiv:1606.04130v52016
Understanding Alternating Minimization for Matrix Completion
Moritz Hardt
cs.LGcs.DSstat.MLarXiv:1312.0925v42013
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
Jiangtao Xie, Fei Long, Jiaming Lv +2
cs.CVcs.LGstat.MLarXiv:2204.04567v12022
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya +6
eess.IVcs.LGstat.MLarXiv:1906.04045v22019
Knowledge Tracing with Sequential Key-Value Memory Networks
Ghodai Abdelrahman, Qing Wang
cs.LGcs.AIcs.IRarXiv:1910.13197v12019
Tensor Completion Algorithms in Big Data Analytics
Qingquan Song, Hancheng Ge, James Caverlee +1
stat.MLcs.AIcs.LGarXiv:1711.10105v22017
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation
Marco Ancona, Cengiz Öztireli, Markus Gross
cs.LGstat.MLarXiv:1903.10992v42019
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li, Lijun Li, Liqiang Wang +2
cs.LGcs.CRcs.CVarXiv:1905.00441v32019
ARock: an Algorithmic Framework for Asynchronous Parallel Coordinate Updates
Zhimin Peng, Yangyang Xu, Ming Yan +1
math.OCcs.DCstat.MLarXiv:1506.02396v52015
RLHF Workflow: From Reward Modeling to Online RLHF
Hanze Dong, Wei Xiong, Bo Pang +7
cs.LGcs.AIcs.CLarXiv:2405.07863v32024
Deep Deterministic Uncertainty: A Simple Baseline
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort +2
cs.LGstat.MLarXiv:2102.11582v32021
Private Stochastic Convex Optimization with Optimal Rates
Raef Bassily, Vitaly Feldman, Kunal Talwar +1
cs.LGcs.CRcs.DSarXiv:1908.09970v12019
An Introductory Survey on Attention Mechanisms in NLP Problems
Dichao Hu
cs.CLcs.LGstat.MLarXiv:1811.05544v12018
Multi-objective Evolutionary Federated Learning
Hangyu Zhu, Yaochu Jin
cs.LGcs.AIstat.MLarXiv:1812.07478v22018
Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control
Amirmohammad Farzaneh, Osvaldo Simeone
stat.MLcs.AIcs.ITarXiv:2608.28179v12026
Leveraging BERT for Extractive Text Summarization on Lectures
Derek Miller
cs.CLcs.LGcs.SDarXiv:1906.04165v12019
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles +1
stat.MLcs.DCcs.ITarXiv:1803.09877v42018
Adversarially Robust Distillation
Micah Goldblum, Liam Fowl, Soheil Feizi +1
cs.LGcs.CVstat.MLarXiv:1905.09747v22019
On the Universality of Invariant Networks
Haggai Maron, Ethan Fetaya, Nimrod Segol +1
cs.LGstat.MLarXiv:1901.09342v42019
Performative Privacy: When Differential Privacy Maximizes Utility
Uddalak Mukherjee, Edwige Cyffers, Yann Chevaleyre
cs.LGcs.AIstat.MLarXiv:2608.28198v12026
Deep Learning Models of the Retinal Response to Natural Scenes
Lane T. McIntosh, Niru Maheswaranathan, Aran Nayebi +2
q-bio.NCstat.MLarXiv:1702.01825v12017
Improve Unsupervised Domain Adaptation with Mixup Training
Shen Yan, Huan Song, Nanxiang Li +2
stat.MLcs.CVcs.LGarXiv:2001.00677v12020
Understanding Self-Training for Gradual Domain Adaptation
Ananya Kumar, Tengyu Ma, Percy Liang
cs.LGstat.MLarXiv:2002.11361v12020
Functional Variational Bayesian Neural Networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi +1
cs.LGstat.MLarXiv:1903.05779v12019
An Exponential Learning Rate Schedule for Deep Learning
Zhiyuan Li, Sanjeev Arora
cs.LGstat.MLarXiv:1910.07454v32019
DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, Susan B. Davidson
cs.LGstat.MLarXiv:2006.14755v22020
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Edward Choi, Cao Xiao, Walter F. Stewart +1
cs.LGcs.CLstat.MLarXiv:1810.09593v12018
Learning Latent Tree Graphical Models
Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar +1
stat.MLcs.ITarXiv:1009.2722v12010
A Survey on Methods and Theories of Quantized Neural Networks
Yunhui Guo
cs.LGcs.NEstat.MLarXiv:1808.04752v22018
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
Tianrong Chen, Guan-Horng Liu, Evangelos A. Theodorou
stat.MLcs.LGmath.AParXiv:2110.11291v52021
Bridging the Gap between Training and Inference for Neural Machine Translation
Wen Zhang, Yang Feng, Fandong Meng +2
cs.CLcs.LGstat.MLarXiv:1906.02448v22019
Learning Disentangled Joint Continuous and Discrete Representations
Emilien Dupont
stat.MLcs.LGarXiv:1804.00104v32018
Monotonic Chunkwise Attention
Chung-Cheng Chiu, Colin Raffel
cs.CLstat.MLarXiv:1712.05382v22017
Goal-conditioned Imitation Learning
Yiming Ding, Carlos Florensa, Mariano Phielipp +1
cs.LGcs.AIcs.NEarXiv:1906.05838v32019
Gradient Descent Learns Linear Dynamical Systems
Moritz Hardt, Tengyu Ma, Benjamin Recht
cs.LGcs.DSmath.OCarXiv:1609.05191v22016
Deep Neural Network Architectures for Modulation Classification
Xiaoyu Liu, Diyu Yang, Aly El Gamal
cs.LGstat.MLarXiv:1712.00443v32017
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method
Simon Lacoste-Julien, Mark Schmidt, Francis Bach
cs.LGmath.OCstat.MLarXiv:1212.2002v22012
Soft Threshold Weight Reparameterization for Learnable Sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani +4
cs.LGcs.CVstat.MLarXiv:2002.03231v92020
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression
Ian Covert, Su-In Lee
cs.LGstat.MLarXiv:2012.01536v32020
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Adam R. Kosiorek, Hyunjik Kim, Ingmar Posner +1
cs.LGcs.CVstat.MLarXiv:1806.01794v22018
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Qingsong Wen, Jingkun Gao, Xiaomin Song +3
cs.LGeess.SPstat.AParXiv:1812.01767v12018
On Tensor Completion via Nuclear Norm Minimization
Ming Yuan, Cun-Hui Zhang
stat.MLcs.ITmath.NAarXiv:1405.1773v12014
Stationary signal processing on graphs
Nathanaël Perraudin, Pierre Vandergheynst
cs.DSstat.APstat.MLarXiv:1601.02522v52016
Symplectic Recurrent Neural Networks
Zhengdao Chen, Jianyu Zhang, Martin Arjovsky +1
cs.LGstat.MLarXiv:1909.13334v22019
ProjE: Embedding Projection for Knowledge Graph Completion
Baoxu Shi, Tim Weninger
cs.AIstat.MLarXiv:1611.05425v12016
Conditional Gaussian Distribution Learning for Open Set Recognition
Xin Sun, Zhenning Yang, Chi Zhang +2
cs.LGstat.MLarXiv:2003.08823v42020
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Chuizheng Meng, Sungyong Seo, Defu Cao +2
cs.LGstat.MLarXiv:2203.16797v12022
Quantum machine learning beyond kernel methods
Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup +3
quant-phcs.AIcs.LGarXiv:2110.13162v32021
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Urs Köster, Tristan J. Webb, Xin Wang +11
cs.LGmath.NAstat.MLarXiv:1711.02213v22017
Sliced Wasserstein Kernel for Persistence Diagrams
Mathieu Carrière, Marco Cuturi, Steve Oudot
cs.CGmath.ATstat.MLarXiv:1706.03358v32017
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
Rohit Babbar, Bernhard Shoelkopf
stat.MLcs.LGarXiv:1609.02521v12016
Training Neural Networks with Local Error Signals
Arild Nøkland, Lars Hiller Eidnes
stat.MLcs.CVcs.LGarXiv:1901.06656v22019
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Christos Louizos, Max Welling
stat.MLcs.LGarXiv:1603.04733v52016

Machine Learning (stat)

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

Defending against Backdoors in Federated Learning with Robust Learning Rate

Measuring the tendency of CNNs to Learn Surface Statistical Regularities

Random Feature Maps for Dot Product Kernels

Implicit Reparameterization Gradients

Large-Scale Methods for Distributionally Robust Optimization

Learning Likelihoods with Conditional Normalizing Flows

Modeling Missing Data in Clinical Time Series with RNNs

Understanding Alternating Minimization for Matrix Completion

Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification

PHiSeg: Capturing Uncertainty in Medical Image Segmentation

Knowledge Tracing with Sequential Key-Value Memory Networks

Tensor Completion Algorithms in Big Data Analytics

Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

ARock: an Algorithmic Framework for Asynchronous Parallel Coordinate Updates

RLHF Workflow: From Reward Modeling to Online RLHF

Deep Deterministic Uncertainty: A Simple Baseline

Private Stochastic Convex Optimization with Optimal Rates

An Introductory Survey on Attention Mechanisms in NLP Problems

Multi-objective Evolutionary Federated Learning

Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control

Leveraging BERT for Extractive Text Summarization on Lectures

DRACO: Byzantine-resilient Distributed Training via Redundant Gradients

Adversarially Robust Distillation

On the Universality of Invariant Networks

Performative Privacy: When Differential Privacy Maximizes Utility

Deep Learning Models of the Retinal Response to Natural Scenes

Improve Unsupervised Domain Adaptation with Mixup Training

Understanding Self-Training for Gradual Domain Adaptation

Functional Variational Bayesian Neural Networks

An Exponential Learning Rate Schedule for Deep Learning

DeltaGrad: Rapid retraining of machine learning models

MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare

Learning Latent Tree Graphical Models

A Survey on Methods and Theories of Quantized Neural Networks

Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory

Bridging the Gap between Training and Inference for Neural Machine Translation

Learning Disentangled Joint Continuous and Discrete Representations

Monotonic Chunkwise Attention

Goal-conditioned Imitation Learning

Gradient Descent Learns Linear Dynamical Systems

Deep Neural Network Architectures for Modulation Classification

A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method

Soft Threshold Weight Reparameterization for Learnable Sparsity

Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression

Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

On Tensor Completion via Nuclear Norm Minimization

Stationary signal processing on graphs

Symplectic Recurrent Neural Networks

ProjE: Embedding Projection for Knowledge Graph Completion

Conditional Gaussian Distribution Learning for Open Set Recognition

When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning

Quantum machine learning beyond kernel methods

Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks

Sliced Wasserstein Kernel for Persistence Diagrams

DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

Training Neural Networks with Local Error Signals

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors