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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3,241 to 3,300 of 6,784

From Bandits to Experts: On the Value of Side-Observations
Shie Mannor, Ohad Shamir
cs.LGstat.MLarXiv:1106.2436v32011
Retrosynthesis Prediction with Conditional Graph Logic Network
Hanjun Dai, Chengtao Li, Connor W. Coley +2
cs.LGstat.MLarXiv:2001.01408v12020
To Cluster, or Not to Cluster: An Analysis of Clusterability Methods
A. Adolfsson, M. Ackerman, N. C. Brownstein
stat.MLcs.LGarXiv:1808.08317v12018
Synthesizing Programs for Images using Reinforced Adversarial Learning
Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin +2
cs.CVcs.LGstat.MLarXiv:1804.01118v12018
Verifying Properties of Binarized Deep Neural Networks
Nina Narodytska, Shiva Prasad Kasiviswanathan, Leonid Ryzhyk +2
stat.MLcs.AIcs.CRarXiv:1709.06662v22017
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes
Lei Wu, Zhanxing Zhu, Weinan E
cs.LGcs.AIstat.MLarXiv:1706.10239v22017
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI
Endre Grøvik, Darvin Yi, Michael Iv +3
eess.IVcs.LGstat.MLarXiv:1903.07988v12019
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen +5
cs.LGstat.MLarXiv:2005.07186v22020
Inferring clonal evolution of tumors from single nucleotide somatic mutations
Wei Jiao, Shankar Vembu, Amit G. Deshwar +2
cs.LGq-bio.PEq-bio.QMarXiv:1210.3384v42012
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Marc Finzi, Max Welling, Andrew Gordon Wilson
cs.LGmath.DSstat.MLarXiv:2104.09459v12021
No bad local minima: Data independent training error guarantees for multilayer neural networks
Daniel Soudry, Yair Carmon
stat.MLcs.LGcs.NEarXiv:1605.08361v22016
Active Deep Learning for Classification of Hyperspectral Images
Peng Liu, Hui Zhang, Kie B. Eom
cs.LGcs.CVstat.MLarXiv:1611.10031v12016
Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering
Yang Wang, Wenjie Zhang, Lin Wu +3
cs.LGstat.MLarXiv:1608.05560v12016
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
Jie Zhou, Xu Han, Cheng Yang +4
cs.CLcs.AIcs.LGarXiv:1908.01843v12019
Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
David Schnoerr, Guido Sanguinetti, Ramon Grima
q-bio.QMcond-mat.stat-mechphysics.bio-pharXiv:1608.06582v22016
Equivalence of restricted Boltzmann machines and tensor network states
Jing Chen, Song Cheng, Haidong Xie +2
cond-mat.str-elquant-phstat.MLarXiv:1701.04831v22017
NeVAE: A Deep Generative Model for Molecular Graphs
Bidisha Samanta, Abir De, Gourhari Jana +3
cs.LGphysics.soc-phstat.MLarXiv:1802.05283v42018
Benchmarking Deep Learning Interpretability in Time Series Predictions
Aya Abdelsalam Ismail, Mohamed Gunady, Héctor Corrada Bravo +1
cs.LGstat.MLarXiv:2010.13924v12020
A Primer on PAC-Bayesian Learning
Benjamin Guedj
stat.MLcs.LGarXiv:1901.05353v32019
Uncertainty Quantification and Deep Ensembles
Rahul Rahaman, Alexandre H. Thiery
stat.MLcs.LGarXiv:2007.08792v42020
The Lipschitz Constant of Self-Attention
Hyunjik Kim, George Papamakarios, Andriy Mnih
stat.MLcs.LGarXiv:2006.04710v22020
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Simon S. Du, Jason D. Lee, Yuandong Tian +2
cs.LGcs.AIcs.CVarXiv:1712.00779v22017
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudik
stat.MLcs.LGarXiv:1612.01205v22016
OpenML Benchmarking Suites
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer +6
stat.MLcs.LGarXiv:1708.03731v32017
RNADE: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, Hugo Larochelle
stat.MLcs.LGarXiv:1306.0186v22013
Composite Binary Losses
Mark D. Reid, Robert C. Williamson
stat.MLarXiv:0912.3301v12009
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
Xiangning Chen, Cho-Jui Hsieh
cs.LGcs.CVstat.MLarXiv:2002.05283v32020
Implicit Semantic Data Augmentation for Deep Networks
Yulin Wang, Xuran Pan, Shiji Song +3
cs.CVcs.LGstat.MLarXiv:1909.12220v52019
Federated Learning for Ultra-Reliable Low-Latency V2V Communications
Sumudu Samarakoon, Mehdi Bennis, Walid Saad +1
cs.NIcs.LGstat.MLarXiv:1805.09253v12018
Communication Algorithms via Deep Learning
Hyeji Kim, Yihan Jiang, Ranvir Rana +3
stat.MLcs.LGarXiv:1805.09317v12018
Question Answering by Reasoning Across Documents with Graph Convolutional Networks
Nicola De Cao, Wilker Aziz, Ivan Titov
cs.CLstat.MLarXiv:1808.09920v42018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Gabrielle Ras, Marcel van Gerven, Pim Haselager
cs.AIcs.LGstat.MLarXiv:1803.07517v22018
On the Origin of Deep Learning
Haohan Wang, Bhiksha Raj
cs.LGcs.NEstat.MLarXiv:1702.07800v42017
RuleMatrix: Visualizing and Understanding Classifiers with Rules
Yao Ming, Huamin Qu, Enrico Bertini
cs.LGcs.AIcs.HCarXiv:1807.06228v12018
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Aymeric Dieuleveut, Nicolas Flammarion, Francis Bach
math.OCcs.LGstat.MLarXiv:1602.05419v22016
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal +1
cs.CRstat.MLarXiv:1906.00830v52019
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller +1
cs.LGcs.AIcs.DCarXiv:1805.08768v12018
Feature Engineering for Predictive Modeling using Reinforcement Learning
Udayan Khurana, Horst Samulowitz, Deepak Turaga
cs.AIcs.LGstat.MLarXiv:1709.07150v12017
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
Ruochi Zhang, Yuesong Zou, Jian Ma
cs.LGstat.MLarXiv:1911.02613v12019
Automatic Variational Inference in Stan
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman +1
stat.MLarXiv:1506.03431v22015
Learning Features of Music from Scratch
John Thickstun, Zaid Harchaoui, Sham Kakade
stat.MLcs.LGcs.SDarXiv:1611.09827v22016
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
Soochan Lee, Junsoo Ha, Dongsu Zhang +1
cs.LGcs.NEstat.MLarXiv:2001.00689v22020
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth +2
stat.MLcs.LGq-bio.BMarXiv:2306.17775v22023
On Biased Compression for Distributed Learning
Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik +1
cs.LGcs.DCmath.OCarXiv:2002.12410v42020
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Mingyang Chen, Wen Zhang, Wei Zhang +2
cs.CLstat.MLarXiv:1909.01515v12019
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou, Sharan Vaswani, Issam Laradji +1
math.OCcs.LGstat.MLarXiv:2002.10542v32020
Deep Reinforcement Learning For Sequence to Sequence Models
Yaser Keneshloo, Tian Shi, Naren Ramakrishnan +1
cs.LGstat.MLarXiv:1805.09461v42018
The State of the Art in Integrating Machine Learning into Visual Analytics
A. Endert, W. Ribarsky, C. Turkay +4
stat.MLcs.HCcs.LGarXiv:1802.07954v12018
Wav-KAN: Wavelet Kolmogorov-Arnold Networks
Zavareh Bozorgasl, Hao Chen
cs.LGcs.AIeess.SParXiv:2405.12832v22024
Relational Deep Reinforcement Learning
Vinicius Zambaldi, David Raposo, Adam Santoro +13
cs.LGstat.MLarXiv:1806.01830v22018
Semi-Stochastic Gradient Descent Methods
Jakub Konečný, Peter Richtárik
stat.MLcs.LGmath.NAarXiv:1312.1666v22013
Simple And Efficient Architecture Search for Convolutional Neural Networks
Thomas Elsken, Jan-Hendrik Metzen, Frank Hutter
stat.MLcs.AIcs.LGarXiv:1711.04528v12017
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Guansong Pang, Longbing Cao, Ling Chen +1
cs.LGcs.AIcs.DBarXiv:1806.04808v12018
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Juyeon Heo, Sunghwan Joo, Taesup Moon
cs.LGcs.AIcs.CVarXiv:1902.02041v32019
Human brain distinctiveness based on EEG spectral coherence connectivity
Daria La Rocca, Patrizio Campisi, Balazs Vegso +4
q-bio.NCstat.MLarXiv:1403.6384v12014
Multi-Task Reinforcement Learning with Soft Modularization
Ruihan Yang, Huazhe Xu, Yi Wu +1
cs.LGcs.AIcs.ROarXiv:2003.13661v22020
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
Qin Zou, Yanling Wang, Qian Wang +2
cs.LGeess.SPstat.MLarXiv:1811.00338v32018
RvS: What is Essential for Offline RL via Supervised Learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov +1
cs.LGcs.AIstat.MLarXiv:2112.10751v22021
Automatic Curriculum Learning For Deep RL: A Short Survey
Rémy Portelas, Cédric Colas, Lilian Weng +2
cs.LGcs.AIstat.MLarXiv:2003.04664v22020
Orthogonal Statistical Learning
Dylan J. Foster, Vasilis Syrgkanis
math.STcs.LGecon.EMarXiv:1901.09036v42019

Machine Learning (stat)

From Bandits to Experts: On the Value of Side-Observations

Retrosynthesis Prediction with Conditional Graph Logic Network

To Cluster, or Not to Cluster: An Analysis of Clusterability Methods

Synthesizing Programs for Images using Reinforced Adversarial Learning

Verifying Properties of Binarized Deep Neural Networks

Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

Inferring clonal evolution of tumors from single nucleotide somatic mutations

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

No bad local minima: Data independent training error guarantees for multilayer neural networks

Active Deep Learning for Classification of Hyperspectral Images

Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering

GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification

Approximation and inference methods for stochastic biochemical kinetics - a tutorial review

Equivalence of restricted Boltzmann machines and tensor network states

NeVAE: A Deep Generative Model for Molecular Graphs

Benchmarking Deep Learning Interpretability in Time Series Predictions

A Primer on PAC-Bayesian Learning

Uncertainty Quantification and Deep Ensembles

The Lipschitz Constant of Self-Attention

Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima

Optimal and Adaptive Off-policy Evaluation in Contextual Bandits

OpenML Benchmarking Suites

RNADE: The real-valued neural autoregressive density-estimator

Composite Binary Losses

Stabilizing Differentiable Architecture Search via Perturbation-based Regularization

Implicit Semantic Data Augmentation for Deep Networks

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

Communication Algorithms via Deep Learning

Question Answering by Reasoning Across Documents with Graph Convolutional Networks

Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

On the Origin of Deep Learning

RuleMatrix: Visualizing and Understanding Classifiers with Rules

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

DAWN: Dynamic Adversarial Watermarking of Neural Networks

Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication

Feature Engineering for Predictive Modeling using Reinforcement Learning

Hyper-SAGNN: a self-attention based graph neural network for hypergraphs

Automatic Variational Inference in Stan

Learning Features of Music from Scratch

A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models

On Biased Compression for Distributed Learning

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence

Deep Reinforcement Learning For Sequence to Sequence Models

The State of the Art in Integrating Machine Learning into Visual Analytics

Wav-KAN: Wavelet Kolmogorov-Arnold Networks

Relational Deep Reinforcement Learning

Semi-Stochastic Gradient Descent Methods

Simple And Efficient Architecture Search for Convolutional Neural Networks

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

Fooling Neural Network Interpretations via Adversarial Model Manipulation

Human brain distinctiveness based on EEG spectral coherence connectivity

Multi-Task Reinforcement Learning with Soft Modularization

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

RvS: What is Essential for Offline RL via Supervised Learning?

Automatic Curriculum Learning For Deep RL: A Short Survey

Orthogonal Statistical Learning