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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7,441 to 7,500 of 9,646

Universal Transformers
Mostafa Dehghani, Stephan Gouws, Oriol Vinyals +2
cs.CLcs.LGstat.MLarXiv:1807.03819v32018
Generalized End-to-End Loss for Speaker Verification
Li Wan, Quan Wang, Alan Papir +1
eess.AScs.CLcs.LGarXiv:1710.10467v52017
Elliptical slice sampling
Iain Murray, Ryan Prescott Adams, David J. C. MacKay
stat.COstat.MLarXiv:1001.0175v22009
Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent
Aleksandar Botev, Guy Lever, David Barber
stat.MLcs.LGarXiv:1607.01981v22016
Evaluation Metrics for Unsupervised Learning Algorithms
Julio-Omar Palacio-Niño, Fernando Berzal
cs.LGstat.MLarXiv:1905.05667v22019
Search Dimension in Unlabeled Projection Pursuit: A Scaling Law for Subspace Restriction
Rares Dimitrie Grozavescu, Mark Girolami
cs.LGmath.STstat.MLarXiv:2609.37917v12026
Finite-Sample Approximation of Hessian-Guided Perturbed Wasserstein Gradient Flows
Ryotaro Kawata, Atsushi Nitanda, Taiji Suzuki
cs.LGstat.MLarXiv:2610.10218v12026
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
James Martens, Roger Grosse
cs.LGcs.NEstat.MLarXiv:1503.05671v72015
On the Convergence Rate of Training Recurrent Neural Networks
Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song
cs.LGcs.DScs.NEarXiv:1810.12065v42018
ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining
Jiefeng Chen, Yixuan Li, Xi Wu +2
cs.LGstat.MLarXiv:2006.15207v42020
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Judea Pearl
cs.LGcs.AIstat.MLarXiv:1801.04016v12018
BRIDGE: Byzantine-resilient Decentralized Gradient Descent
Cheng Fang, Zhixiong Yang, Waheed U. Bajwa
stat.MLcs.DCcs.LGarXiv:1908.08098v32019
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Juyeon Heo, Sunghwan Joo, Taesup Moon
cs.LGcs.AIcs.CVarXiv:1902.02041v32019
Improving GANs Using Optimal Transport
Tim Salimans, Han Zhang, Alec Radford +1
cs.LGstat.MLarXiv:1803.05573v12018
Learning Optimal Conformal Classifiers
David Stutz, Krishnamurthy, Dvijotham +2
cs.LGcs.CVstat.MEarXiv:2110.09192v32021
Kinetic Langevin Meets Split Gibbs: Accelerated Posterior Sampling for Imaging Inverse Problems with Diffusion Priors
Dai Hai Nguyen, Duc Dung Nguyen
stat.MLcs.LGarXiv:2610.10187v12026
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
Yang You, Jing Li, Sashank Reddi +7
cs.LGcs.AIcs.CLarXiv:1904.00962v52019
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
Arka Daw, Anuj Karpatne, William Watkins +2
cs.LGcs.AIcs.CVarXiv:1710.11431v32017
Pre-training via Paraphrasing
Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh +3
cs.CLcs.LGstat.MLarXiv:2006.15020v12020
Deep Learning on Graphs: A Survey
Ziwei Zhang, Peng Cui, Wenwu Zhu
cs.LGcs.SIstat.MLarXiv:1812.04202v32018
Protection Against Reconstruction and Its Applications in Private Federated Learning
Abhishek Bhowmick, John Duchi, Julien Freudiger +2
stat.MLcs.LGarXiv:1812.00984v22018
Reinforcement Learning with Augmented Data
Michael Laskin, Kimin Lee, Adam Stooke +3
cs.LGstat.MLarXiv:2004.14990v52020
Horovod: fast and easy distributed deep learning in TensorFlow
Alexander Sergeev, Mike Del Balso
cs.LGstat.MLarXiv:1802.05799v32018
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Tengfei Ma, Jie Chen, Cao Xiao
cs.LGstat.MLarXiv:1809.02630v22018
Winner's Curse Drives False Promises in Data-Driven Decisions: A Case Study in Refugee Matching
Hamsa Bastani, Osbert Bastani, Bryce McLaughlin
stat.MLcs.LGecon.EMarXiv:2602.08892v12026
Graph U-Nets
Hongyang Gao, Shuiwang Ji
cs.LGstat.MLarXiv:1905.05178v12019
Multi-view Low-rank Sparse Subspace Clustering
Maria Brbic, Ivica Kopriva
cs.CVcs.LGmath.OCarXiv:1708.08732v12017
Communication Algorithms via Deep Learning
Hyeji Kim, Yihan Jiang, Ranvir Rana +3
stat.MLcs.LGarXiv:1805.09317v12018
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl +4
cs.LGcs.CRstat.MLarXiv:1811.04017v22018
Discrete Diffusion Models via Evolving Variational Autoregressive Networks
Kewen Pan, Ying Tang
cs.LGcond-mat.dis-nnstat.MLarXiv:2609.27306v12026
ECG Heartbeat Classification: A Deep Transferable Representation
Mohammad Kachuee, Shayan Fazeli, Majid Sarrafzadeh
cs.CYcs.LGstat.MLarXiv:1805.00794v22018
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Weihua Hu, Takeru Miyato, Seiya Tokui +2
stat.MLcs.LGarXiv:1702.08720v32017
Forward and Reverse Gradient-Based Hyperparameter Optimization
Luca Franceschi, Michele Donini, Paolo Frasconi +1
stat.MLarXiv:1703.01785v32017
Rademacher Complexity for Adversarially Robust Generalization
Dong Yin, Kannan Ramchandran, Peter Bartlett
cs.LGcs.CRcs.NEarXiv:1810.11914v42018
Measuring the Intrinsic Dimension of Objective Landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu +1
cs.LGcs.NEstat.MLarXiv:1804.08838v12018
Self-attention for raw optical Satellite Time Series Classification
Marc Rußwurm, Marco Körner
cs.LGeess.IVstat.MLarXiv:1910.10536v32019
DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, Susan B. Davidson
cs.LGstat.MLarXiv:2006.14755v22020
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Christoph Molnar, Timo Freiesleben, Gunnar König +3
stat.MLcs.LGarXiv:2109.01433v12021
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Pengfei Chen, Benben Liao, Guangyong Chen +1
cs.LGstat.MLarXiv:1905.05040v12019
Value Iteration Networks
Aviv Tamar, Yi Wu, Garrett Thomas +2
cs.AIcs.LGcs.NEarXiv:1602.02867v42016
AdaCliP: Adaptive Clipping for Private SGD
Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu +2
cs.LGcs.CRstat.MLarXiv:1908.07643v22019
Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics
Vinay V. Ramasesh, Ethan Dyer, Maithra Raghu
cs.LGcs.CVstat.MLarXiv:2007.07400v12020
Iterative Gaussianization: from ICA to Random Rotations
Valero Laparra, Gustavo Camps-Valls, Jesús Malo
stat.MLarXiv:1602.00229v12016
Kernels for sequentially ordered data
Franz J Király, Harald Oberhauser
stat.MLcs.DMcs.LGarXiv:1601.08169v12016
Adaptive Machine Unlearning
Varun Gupta, Christopher Jung, Seth Neel +3
cs.LGstat.MLarXiv:2106.04378v12021
Understanding Tool-Integrated Reasoning
Heng Lin, Zhongwen Xu
cs.LGcs.AIstat.MLarXiv:2508.19201v12025
Strategic Classification is Causal Modeling in Disguise
John Miller, Smitha Milli, Moritz Hardt
cs.LGstat.MLarXiv:1910.10362v32019
A stochastic subgradient method with optimal failure exponent
Bart P. G. van Parys
math.OCstat.MLarXiv:2609.37425v12026
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Bryan Wilder, Bistra Dilkina, Milind Tambe
cs.LGcs.AIstat.MLarXiv:1809.05504v22018
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
Jeff Z. HaoChen, Colin Wei, Adrien Gaidon +1
cs.LGstat.MLarXiv:2106.04156v72021
Identifiability of a dissipative knowledge-dynamics model: exact recovery under designed excitation, degeneration on observational data
Arman Kostanian, Armen Beklaryan
cs.LGmath.OCstat.MLarXiv:2610.09889v12026
Sparse Separable Factor Analysis in the Complex Domain with an Application to Local Field Potential Data
Ian Hultman, Kirtikanth Kalapatapu, Yassine Filali +2
stat.MLcs.LGstat.COarXiv:2608.21551v12026
Continual Learning in Generative Adversarial Nets
Ari Seff, Alex Beatson, Daniel Suo +1
cs.LGcs.AIstat.MLarXiv:1705.08395v12017
Sparsifying Stochasticity, Not Capacity: Partial Stochasticity via Deep Weight Factorization of Prior Scales
Marius P Linhard, Maurizio Filippone
stat.MLcs.LGarXiv:2610.09886v12026
Nonlinear Interference Mitigation via Deep Neural Networks
Christian Häger, Henry D. Pfister
cs.ITstat.MLarXiv:1710.06234v12017
Deep reinforcement learning from human preferences
Paul Christiano, Jan Leike, Tom B. Brown +3
stat.MLcs.AIcs.HCarXiv:1706.03741v42017
Robust Kernel Density Estimation
JooSeuk Kim, Clayton D. Scott
stat.MLcs.LGstat.MEarXiv:1107.3133v22011
Flow Matching on General Geometries
Ricky T. Q. Chen, Yaron Lipman
cs.LGcs.AIstat.MLarXiv:2302.03660v32023
Model-Based Reinforcement Learning via Meta-Policy Optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman +3
cs.LGcs.AIstat.MLarXiv:1809.05214v12018
The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes
Nicholas Heller, Niranjan Sathianathen, Arveen Kalapara +17
q-bio.QMcs.LGstat.MLarXiv:1904.00445v22019

Machine Learning (stat)

Universal Transformers

Generalized End-to-End Loss for Speaker Verification

Elliptical slice sampling

Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent

Evaluation Metrics for Unsupervised Learning Algorithms

Search Dimension in Unlabeled Projection Pursuit: A Scaling Law for Subspace Restriction

Finite-Sample Approximation of Hessian-Guided Perturbed Wasserstein Gradient Flows

Optimizing Neural Networks with Kronecker-factored Approximate Curvature

On the Convergence Rate of Training Recurrent Neural Networks

ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining

Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution

BRIDGE: Byzantine-resilient Decentralized Gradient Descent

Fooling Neural Network Interpretations via Adversarial Model Manipulation

Improving GANs Using Optimal Transport

Learning Optimal Conformal Classifiers

Kinetic Langevin Meets Split Gibbs: Accelerated Posterior Sampling for Imaging Inverse Problems with Diffusion Priors

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

Pre-training via Paraphrasing

Deep Learning on Graphs: A Survey

Protection Against Reconstruction and Its Applications in Private Federated Learning

Reinforcement Learning with Augmented Data

Horovod: fast and easy distributed deep learning in TensorFlow

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

Winner's Curse Drives False Promises in Data-Driven Decisions: A Case Study in Refugee Matching

Graph U-Nets

Multi-view Low-rank Sparse Subspace Clustering

Communication Algorithms via Deep Learning

A generic framework for privacy preserving deep learning

Discrete Diffusion Models via Evolving Variational Autoregressive Networks

ECG Heartbeat Classification: A Deep Transferable Representation

Learning Discrete Representations via Information Maximizing Self-Augmented Training

Forward and Reverse Gradient-Based Hyperparameter Optimization

Rademacher Complexity for Adversarially Robust Generalization

Measuring the Intrinsic Dimension of Objective Landscapes

Self-attention for raw optical Satellite Time Series Classification

DeltaGrad: Rapid retraining of machine learning models

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

Value Iteration Networks

AdaCliP: Adaptive Clipping for Private SGD

Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics

Iterative Gaussianization: from ICA to Random Rotations

Kernels for sequentially ordered data

Adaptive Machine Unlearning

Understanding Tool-Integrated Reasoning

Strategic Classification is Causal Modeling in Disguise

A stochastic subgradient method with optimal failure exponent

Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

Identifiability of a dissipative knowledge-dynamics model: exact recovery under designed excitation, degeneration on observational data

Sparse Separable Factor Analysis in the Complex Domain with an Application to Local Field Potential Data

Continual Learning in Generative Adversarial Nets

Sparsifying Stochasticity, Not Capacity: Partial Stochasticity via Deep Weight Factorization of Prior Scales

Nonlinear Interference Mitigation via Deep Neural Networks

Deep reinforcement learning from human preferences

Robust Kernel Density Estimation

Flow Matching on General Geometries

Model-Based Reinforcement Learning via Meta-Policy Optimization

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes