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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8,701 to 8,760 of 9,674

TUDataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause +3
cs.LGcs.NEstat.MLarXiv:2007.08663v12020
Recent Advances in Deep Learning: An Overview
Matiur Rahman Minar, Jibon Naher
cs.LGcs.AIstat.MLarXiv:1807.08169v12018
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
George C. Linderman, Manas Rachh, Jeremy G. Hoskins +2
cs.LGstat.MLarXiv:1712.09005v12017
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Qiyang Li, Saminul Haque, Cem Anil +3
cs.LGstat.MLarXiv:1911.00937v22019
TRACE: Retrospective Streaming Generation of Physical Fields under Sparse Structured Sensing
Xinyu Zhang, Lihao Chen, Panqi Chen +4
stat.MLcs.LGarXiv:2608.26219v12026
Predicting with Confidence on Unseen Distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi +2
cs.LGcs.CVstat.MLarXiv:2107.03315v22021
Autonomous discovery in the chemical sciences part I: Progress
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
q-bio.QMcs.AIcs.ROarXiv:2003.13754v12020
DC3: A learning method for optimization with hard constraints
Priya L. Donti, David Rolnick, J. Zico Kolter
cs.LGmath.OCstat.MLarXiv:2104.12225v12021
Recurrent Models of Visual Attention
Volodymyr Mnih, Nicolas Heess, Alex Graves +1
cs.LGcs.CVstat.MLarXiv:1406.6247v12014
Building powerful and equivariant graph neural networks with structural message-passing
Clement Vignac, Andreas Loukas, Pascal Frossard
cs.LGstat.MLarXiv:2006.15107v32020
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path
X. Y. Han, Vardan Papyan, David L. Donoho
cs.LGcs.AImath.DGarXiv:2106.02073v42021
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Michihiro Yasunaga, Percy Liang
cs.SEcs.CLcs.LGarXiv:2005.10636v22020
Training-image based geostatistical inversion using a spatial generative adversarial neural network
Eric Laloy, Romain Hérault, Diederik Jacques +1
stat.MLcs.CVphysics.geo-pharXiv:1708.04975v22017
MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Chengxi Zang, Fei Wang
stat.MLcs.LGphysics.chem-pharXiv:2006.10137v12020
Federated Learning with Bayesian Differential Privacy
Aleksei Triastcyn, Boi Faltings
cs.LGcs.CRcs.DCarXiv:1911.10071v12019
Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, Pavel Laskov
cs.LGcs.CRstat.MLarXiv:1206.6389v32012
A Robust Learning Approach to Domain Adaptive Object Detection
Mehran Khodabandeh, Arash Vahdat, Mani Ranjbar +1
cs.LGcs.CVstat.MLarXiv:1904.02361v32019
Symmetry-quotient Flatness and Generalization
Taiki Miyagawa
cs.LGstat.MLarXiv:2609.31634v12026
On the Identifiability of the Post-Nonlinear Causal Model
Kun Zhang, Aapo Hyvarinen
stat.MLcs.LGarXiv:1205.2599v12012
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook
cs.LGstat.MLarXiv:2005.10996v12020
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Shiyang Li, Xiaoyong Jin, Yao Xuan +4
cs.LGstat.MLarXiv:1907.00235v32019
Data Mixing as Mixture Experiment: Response Surface Methodology and Optimal Design for Large Language Model Pretraining
Yicheng Mao, Hongru Du
cs.AIstat.MLarXiv:2608.23922v12026
Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe +1
stat.MLcs.LGarXiv:2203.16437v32022
From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion
Satoshi Hayakawa
cs.LGcs.AImath.PRarXiv:2609.01043v12026
Structured Credal Learning
Varun Venkatesh, Eyke Hüllermeier, Bernd Bischl +1
stat.MEstat.MLarXiv:2603.14070v12026
The optimal information complexity of VC learning
Steve Hanneke, Juexiao Wang
stat.MLcs.ITcs.LGarXiv:2610.10600v12026
Large-Scale Optimal Transport and Mapping Estimation
Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary +3
stat.MLarXiv:1711.02283v22017
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data
Jun Yu, HaiYing Wang, Mingyao Ai +1
stat.MEcs.DCstat.COarXiv:2005.10435v32020
Learning Stochastic Recurrent Networks
Justin Bayer, Christian Osendorfer
stat.MLcs.LGarXiv:1411.7610v32014
CONTRA: Conformal Prediction Region via Normalizing Flow Transformation
Zhenhan Fang, Aixin Tan, Jian Huang
stat.MLcs.LGarXiv:2605.08561v12026
The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow
Raphaël Berthier
cs.LGmath.OCstat.MLarXiv:2609.01034v12026
A Distributional Framework for Data Valuation
Amirata Ghorbani, Michael P. Kim, James Zou
cs.LGstat.MLarXiv:2002.12334v12020
Sliced Wasserstein Kernel for Persistence Diagrams
Mathieu Carrière, Marco Cuturi, Steve Oudot
cs.CGmath.ATstat.MLarXiv:1706.03358v32017
Data Poisoning Attacks Against Federated Learning Systems
Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy +1
cs.LGcs.CRstat.MLarXiv:2007.08432v22020
On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
Etienne Casanova, Rafal Kocielnik, R. Michael Alvarez
cs.CLcs.AIcs.LGarXiv:2606.00467v12026
Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks
Jean Hausser, Korbinian Strimmer
stat.MLarXiv:0811.3579v32008
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova +2
stat.MLcs.CVcs.LGarXiv:2002.09103v22020
ModelDiff: A Framework for Comparing Learning Algorithms
Harshay Shah, Sung Min Park, Andrew Ilyas +1
cs.LGcs.CVstat.MLarXiv:2211.12491v12022
Wasserstein-2 Generative Networks
Alexander Korotin, Vage Egiazarian, Arip Asadulaev +2
cs.LGcs.CVstat.MLarXiv:1909.13082v42019
ProMP: Proximal Meta-Policy Search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera +2
cs.LGstat.MLarXiv:1810.06784v42018
Adaptive Multi-Goal Exploration
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard +3
cs.LGstat.MLarXiv:2111.12045v22021
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis +1
cs.LGstat.MLarXiv:1912.02400v22019
A Survey on Neural Architecture Search
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati
cs.LGcs.CVcs.NEarXiv:1905.01392v22019
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
stat.MLcs.LGarXiv:1704.07433v42017
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong +1
cs.LGstat.MLarXiv:2007.08176v22020
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Dan Levi, Liran Gispan, Niv Giladi +1
cs.LGstat.MLarXiv:1905.11659v32019
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits
Julian Zimmert, Yevgeny Seldin
cs.LGstat.MLarXiv:1807.07623v62018
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey +11
cs.LGcs.AIcs.CLarXiv:2404.01413v22024
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
Yan Yan, Tianbao Yang, Zhe Li +2
cs.LGstat.MLarXiv:1808.10396v12018
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Andrew Gordon Wilson, Pavel Izmailov
cs.LGstat.MLarXiv:2002.08791v42020
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes
Pravin Bhasme, Jenil Vagadiya, Udit Bhatia
stat.MLcs.LGstat.AParXiv:2104.11009v12021
GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models
Ziqi Li
cs.LGstat.MLarXiv:2312.03675v22023
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments
Yi Sun, Faustino Gomez, Juergen Schmidhuber
cs.AIstat.MLarXiv:1103.5708v12011
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal +1
cs.LGcs.ROstat.MLarXiv:2006.09359v62020
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
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
Chao Ning, Fengqi You
cs.LGmath.OCstat.MLarXiv:1904.01934v12019
Fast inference of deep neural networks in FPGAs for particle physics
Javier Duarte, Song Han, Philip Harris +8
physics.ins-detcs.CVhep-exarXiv:1804.06913v32018
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer, Mitchell Stern
cs.LGcs.AIstat.MLarXiv:1804.04235v12018
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yuang Jiang, Shiqiang Wang, Victor Valls +4
cs.LGcs.DCstat.MLarXiv:1909.12326v52019
Time-to-Event Prediction with Neural Networks and Cox Regression
Håvard Kvamme, Ørnulf Borgan, Ida Scheel
stat.MLcs.LGarXiv:1907.00825v22019

Machine Learning (stat)

TUDataset: A collection of benchmark datasets for learning with graphs

Recent Advances in Deep Learning: An Overview

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks

TRACE: Retrospective Streaming Generation of Physical Fields under Sparse Structured Sensing

Predicting with Confidence on Unseen Distributions

Autonomous discovery in the chemical sciences part I: Progress

DC3: A learning method for optimization with hard constraints

Recurrent Models of Visual Attention

Building powerful and equivariant graph neural networks with structural message-passing

Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

Training-image based geostatistical inversion using a spatial generative adversarial neural network

MoFlow: An Invertible Flow Model for Generating Molecular Graphs

Federated Learning with Bayesian Differential Privacy

Poisoning Attacks against Support Vector Machines

A Robust Learning Approach to Domain Adaptive Object Detection

Symmetry-quotient Flatness and Generalization

On the Identifiability of the Post-Nonlinear Causal Model

Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

Data Mixing as Mixture Experiment: Response Surface Methodology and Optimal Design for Large Language Model Pretraining

Weakly supervised causal representation learning

From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion

Structured Credal Learning

The optimal information complexity of VC learning

Large-Scale Optimal Transport and Mapping Estimation

Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data

Learning Stochastic Recurrent Networks

CONTRA: Conformal Prediction Region via Normalizing Flow Transformation

The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow

A Distributional Framework for Data Valuation

Sliced Wasserstein Kernel for Persistence Diagrams

Data Poisoning Attacks Against Federated Learning Systems

On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance

Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation

ModelDiff: A Framework for Comparing Learning Algorithms

Wasserstein-2 Generative Networks

ProMP: Proximal Meta-Policy Search

Adaptive Multi-Goal Exploration

Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space

A Survey on Neural Architecture Search

Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Evaluating and Calibrating Uncertainty Prediction in Regression Tasks

Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

A Unified Analysis of Stochastic Momentum Methods for Deep Learning

Bayesian Deep Learning and a Probabilistic Perspective of Generalization

Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes

GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models

Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming

Fast inference of deep neural networks in FPGAs for particle physics

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

Model Pruning Enables Efficient Federated Learning on Edge Devices

Time-to-Event Prediction with Neural Networks and Cox Regression