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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4,021 to 4,080 of 6,792

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
Invariant Risk Minimization Games
Kartik Ahuja, Karthikeyan Shanmugam, Kush R. Varshney +1
cs.LGstat.MLarXiv:2002.04692v22020
Dash: Semi-Supervised Learning with Dynamic Thresholding
Yi Xu, Lei Shang, Jinxing Ye +5
cs.LGcs.CVstat.MLarXiv:2109.00650v12021
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
Hadi Salman, Greg Yang, Huan Zhang +2
cs.LGcs.AIcs.CRarXiv:1902.08722v52019
Does Knowledge Distillation Really Work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko +2
cs.LGstat.MLarXiv:2106.05945v22021
Unsupervised Learning by Predicting Noise
Piotr Bojanowski, Armand Joulin
stat.MLcs.CVcs.LGarXiv:1704.05310v12017
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
Qian Huang, Isay Katsman, Horace He +3
cs.LGcs.CRcs.CVarXiv:1907.10823v32019
CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration
Boah Kim, Dong Hwan Kim, Seong Ho Park +3
cs.CVcs.LGeess.IVarXiv:2008.05772v12020
Graph HyperNetworks for Neural Architecture Search
Chris Zhang, Mengye Ren, Raquel Urtasun
cs.LGcs.CVstat.MLarXiv:1810.05749v32018
Low Latency Privacy Preserving Inference
Alon Brutzkus, Oren Elisha, Ran Gilad-Bachrach
cs.LGstat.MLarXiv:1812.10659v22018
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Cuong V. Nguyen, Tal Hassner, Matthias Seeger +1
cs.LGcs.CVstat.MLarXiv:2002.12462v22020
A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons
Yi-Cheng Chen, Ping-En Lu, Cheng-Shang Chang +1
q-bio.PEcs.LGstat.MLarXiv:2003.00122v62020
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Leila Arras, Franziska Horn, Grégoire Montavon +2
cs.CLcs.IRcs.LGarXiv:1612.07843v12016
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Till Speicher, Hoda Heidari, Nina Grgic-Hlaca +4
cs.LGcs.CYstat.MLarXiv:1807.00787v12018
Gradient descent aligns the layers of deep linear networks
Ziwei Ji, Matus Telgarsky
cs.LGmath.OCstat.MLarXiv:1810.02032v22018
Learning Phase Competition for Traffic Signal Control
Guanjie Zheng, Yuanhao Xiong, Xinshi Zang +6
cs.LGcs.AIstat.MLarXiv:1905.04722v12019
Are adversarial examples inevitable?
Ali Shafahi, W. Ronny Huang, Christoph Studer +2
cs.LGcs.CVstat.MLarXiv:1809.02104v32018
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J. Maddison +2
cs.LGstat.MLarXiv:1703.07370v42017
Generalized Byzantine-tolerant SGD
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
cs.DCstat.MLarXiv:1802.10116v32018
Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?
Boris Hanin
stat.MLcs.LGmath.PRarXiv:1801.03744v32018
Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning
Pan Zhou, Jiashi Feng, Chao Ma +3
cs.LGcs.AImath.OCarXiv:2010.05627v22020
SGD on Neural Networks Learns Functions of Increasing Complexity
Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris +4
cs.LGcs.NEstat.MLarXiv:1905.11604v12019
On Empirical Comparisons of Optimizers for Deep Learning
Dami Choi, Christopher J. Shallue, Zachary Nado +3
cs.LGstat.MLarXiv:1910.05446v32019
The Graphical Lasso: New Insights and Alternatives
Rahul Mazumder, Trevor Hastie
stat.MLcs.LGarXiv:1111.5479v22011
Stochastic Variational Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov +1
stat.MLcs.LGstat.MEarXiv:1611.00336v22016
Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
Taiji Suzuki
stat.MLcs.LGarXiv:1810.08033v12018
Cascading Bandits: Learning to Rank in the Cascade Model
Branislav Kveton, Csaba Szepesvari, Zheng Wen +1
cs.LGstat.MLarXiv:1502.02763v22015
qshap: Fast Shapley Decomposition of $R^2$ for Gradient-Boosted Trees
Zhongli Jiang, Min Zhang, Dabao Zhang
stat.MLcs.LGarXiv:2608.24104v12026
Provably Efficient Exploration in Policy Optimization
Qi Cai, Zhuoran Yang, Chi Jin +1
cs.LGmath.OCstat.MLarXiv:1912.05830v42019
Bounding and Counting Linear Regions of Deep Neural Networks
Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam
cs.LGcs.AIcs.NEarXiv:1711.02114v42017
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
Edward Choi, Zhen Xu, Yujia Li +4
cs.LGstat.MLarXiv:1906.04716v32019
Domain Generalization via Conditional Invariant Representation
Ya Li, Mingming Gong, Xinmei Tian +2
cs.LGcs.CVstat.MLarXiv:1807.08479v12018
A Spectral Algorithm for Latent Dirichlet Allocation
Animashree Anandkumar, Dean P. Foster, Daniel Hsu +2
cs.LGstat.MLarXiv:1204.6703v42012
Memory-Efficient Pipeline-Parallel DNN Training
Deepak Narayanan, Amar Phanishayee, Kaiyu Shi +2
cs.LGcs.DCstat.MLarXiv:2006.09503v32020
Mol-CycleGAN - a generative model for molecular optimization
Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk +2
cs.LGphysics.chem-phstat.MLarXiv:1902.02119v12019
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Sahil Verma, Varich Boonsanong, Minh Hoang +3
cs.LGcs.AIstat.MLarXiv:2010.10596v32020
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications
Onur Avci, Osama Abdeljaber, Serkan Kiranyaz +3
eess.SPcs.LGstat.MLarXiv:2004.04373v12020
A comprehensive review on convolutional neural network in machine fault diagnosis
Jinyang Jiao, Ming Zhao, Jing Lin +1
eess.SPcs.LGstat.MLarXiv:2002.07605v12020
Algorithmic Fairness
Dana Pessach, Erez Shmueli
cs.CYcs.AIcs.LGarXiv:2001.09784v12020
An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yang Yuan +4
cs.LGcs.CVstat.MLarXiv:1806.07755v22018
AutoML: A Survey of the State-of-the-Art
Xin He, Kaiyong Zhao, Xiaowen Chu
cs.LGcs.CVstat.MLarXiv:1908.00709v62019
Deep Learning in Video Multi-Object Tracking: A Survey
Gioele Ciaparrone, Francisco Luque Sánchez, Siham Tabik +3
cs.CVcs.LGstat.MLarXiv:1907.12740v42019
Transfusion: Understanding Transfer Learning for Medical Imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg +1
cs.CVcs.LGstat.MLarXiv:1902.07208v32019
Recent Advances in Autoencoder-Based Representation Learning
Michael Tschannen, Olivier Bachem, Mario Lucic
cs.LGcs.CVstat.MLarXiv:1812.05069v12018
bartMachine: Machine Learning with Bayesian Additive Regression Trees
Adam Kapelner, Justin Bleich
stat.MLcs.LGarXiv:1312.2171v32013
Learning Bayesian Networks with the bnlearn R Package
Marco Scutari
stat.MLarXiv:0908.3817v22009
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent
Frederik Kunstner, Lukas Balles, Philipp Hennig
cs.LGstat.MLarXiv:1905.12558v32019
DC3: A learning method for optimization with hard constraints
Priya L. Donti, David Rolnick, J. Zico Kolter
cs.LGmath.OCstat.MLarXiv:2104.12225v12021

Machine Learning (stat)

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

Invariant Risk Minimization Games

Dash: Semi-Supervised Learning with Dynamic Thresholding

A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks

Does Knowledge Distillation Really Work?

Unsupervised Learning by Predicting Noise

Enhancing Adversarial Example Transferability with an Intermediate Level Attack

CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration

Graph HyperNetworks for Neural Architecture Search

Low Latency Privacy Preserving Inference

LEEP: A New Measure to Evaluate Transferability of Learned Representations

A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons

"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach

A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices

Gradient descent aligns the layers of deep linear networks

Learning Phase Competition for Traffic Signal Control

Are adversarial examples inevitable?

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

Generalized Byzantine-tolerant SGD

Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?

Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning

SGD on Neural Networks Learns Functions of Increasing Complexity

On Empirical Comparisons of Optimizers for Deep Learning

The Graphical Lasso: New Insights and Alternatives

Stochastic Variational Deep Kernel Learning

Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality

Cascading Bandits: Learning to Rank in the Cascade Model

qshap: Fast Shapley Decomposition of $R^2$ for Gradient-Boosted Trees

Provably Efficient Exploration in Policy Optimization

Bounding and Counting Linear Regions of Deep Neural Networks

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

Domain Generalization via Conditional Invariant Representation

A Spectral Algorithm for Latent Dirichlet Allocation

Memory-Efficient Pipeline-Parallel DNN Training

Mol-CycleGAN - a generative model for molecular optimization

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications

A comprehensive review on convolutional neural network in machine fault diagnosis

Algorithmic Fairness

An empirical study on evaluation metrics of generative adversarial networks

AutoML: A Survey of the State-of-the-Art

Deep Learning in Video Multi-Object Tracking: A Survey

Transfusion: Understanding Transfer Learning for Medical Imaging

Recent Advances in Autoencoder-Based Representation Learning

bartMachine: Machine Learning with Bayesian Additive Regression Trees

Learning Bayesian Networks with the bnlearn R Package

Limitations of the Empirical Fisher Approximation for Natural Gradient Descent

DC3: A learning method for optimization with hard constraints