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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7,321 to 7,380 of 9,679

Efficient Dataset Distillation Using Random Feature Approximation
Noel Loo, Ramin Hasani, Alexander Amini +1
cs.LGcs.AIcs.NEarXiv:2210.12067v12022
Optimal Scaling Needs Optimal Norm
Oleg Filatov, Jiangtao Wang, Jan Ebert +1
cs.LGcs.AIstat.MLarXiv:2510.03871v22025
GRAND: Graph Neural Diffusion
Benjamin Paul Chamberlain, James Rowbottom, Maria Gorinova +3
cs.LGstat.MLarXiv:2106.10934v22021
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
Cun Mu, Bo Huang, John Wright +1
stat.MLcs.LGarXiv:1307.5870v22013
Bellman-consistent Pessimism for Offline Reinforcement Learning
Tengyang Xie, Ching-An Cheng, Nan Jiang +2
cs.LGcs.AIstat.MLarXiv:2106.06926v62021
Multiple Instance Learning with Bag Dissimilarities
Veronika Cheplygina, David M. J. Tax, Marco Loog
stat.MLcs.LGarXiv:1309.5643v32013
Adversarial Regularizers in Inverse Problems
Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb
cs.CVcs.LGmath.NAarXiv:1805.11572v22018
Latent mixed-effect models for high-dimensional longitudinal data
Priscilla Ong, Manuel Haußmann, Otto Lönnroth +1
cs.LGstat.APstat.MLarXiv:2409.11008v12024
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Morteza Mardani, Enhao Gong, Joseph Y. Cheng +8
cs.CVcs.LGstat.MLarXiv:1706.00051v12017
Conditional molecular design with deep generative models
Seokho Kang, Kyunghyun Cho
cs.LGstat.MLarXiv:1805.00108v32018
Random sampling of bandlimited signals on graphs
Gilles Puy, Nicolas Tremblay, Rémi Gribonval +1
cs.SIcs.LGstat.MLarXiv:1511.05118v22015
Adaptive Attention Span in Transformers
Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski +1
cs.LGstat.MLarXiv:1905.07799v22019
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Puneet Mangla, Mayank Singh, Abhishek Sinha +3
cs.LGcs.CVstat.MLarXiv:1907.12087v42019
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda +1
cs.LGcs.CVstat.MLarXiv:2005.01807v12020
Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen, Eirikur Agustsson, Mario Lucic
cs.LGstat.MLarXiv:1805.11057v22018
Robust Reinforcement Learning for Continuous Control with Model Misspecification
Daniel J. Mankowitz, Nir Levine, Rae Jeong +7
cs.LGcs.AIstat.MLarXiv:1906.07516v22019
Revisiting Classifier Two-Sample Tests
David Lopez-Paz, Maxime Oquab
stat.MLarXiv:1610.06545v42016
High Fidelity Neural Audio Compression
Alexandre Défossez, Jade Copet, Gabriel Synnaeve +1
eess.AScs.AIcs.SDarXiv:2210.13438v12022
Polynomial-Chaos-based Kriging
R. Schoebi, B. Sudret, J. Wiart
stat.COstat.MEstat.MLarXiv:1502.03939v12015
Parseval Networks: Improving Robustness to Adversarial Examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave +2
stat.MLcs.AIcs.CRarXiv:1704.08847v22017
Deep Reinforcement Learning for Cyber Security
Thanh Thi Nguyen, Vijay Janapa Reddi
cs.CRcs.AIcs.LGarXiv:1906.05799v42019
Deep Anomaly Detection Using Geometric Transformations
Izhak Golan, Ran El-Yaniv
cs.LGstat.MLarXiv:1805.10917v22018
Optimal Clustering Framework for Hyperspectral Band Selection
Qi Wang, Fahong Zhang, Xuelong Li
eess.IVcs.LGstat.MLarXiv:1904.13036v12019
Spectrally-normalized margin bounds for neural networks
Peter Bartlett, Dylan J. Foster, Matus Telgarsky
cs.LGcs.NEstat.MLarXiv:1706.08498v22017
Cascading Bandits: Learning to Rank in the Cascade Model
Branislav Kveton, Csaba Szepesvari, Zheng Wen +1
cs.LGstat.MLarXiv:1502.02763v22015
Proof-of-Learning: Definitions and Practice
Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo +4
cs.LGcs.AIcs.CRarXiv:2103.05633v12021
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
Mathieu Cliche
cs.CLstat.MLarXiv:1704.06125v12017
Flow Matching under Noisy Latent Structure: Beyond Exact Low-Dimensional Support
Lifeng Hao, Shaolin Ji
cs.LGstat.MLarXiv:2609.38918v12026
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Haowei He, Gao Huang, Yang Yuan
cs.LGstat.MLarXiv:1902.00744v22019
AMLB: an AutoML Benchmark
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors +5
cs.LGstat.MLarXiv:2207.12560v22022
A Closed-Form Formula for Consistent Lipschitz Regression on Metric Spaces with Sparse Neural Network Realizations
Ruiyang Hong, Hrad Ghoukasian, Anastasis Kratsios
stat.MLcs.LGarXiv:2609.03129v12026
The Principles of Deep Learning Theory
Daniel A. Roberts, Sho Yaida, Boris Hanin
cs.LGcs.AIhep-tharXiv:2106.10165v22021
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
Ning Miao, Hao Zhou, Lili Mou +2
cs.CLcs.AIcs.LGarXiv:1811.10996v12018
Rare Gate Disagreements Can Limit Plasticity: When Gradient Flow Mispredicts Finite-Batch SGD
Ruoyu Zhao, Mingxuan Zhang, Jianbo Dai +3
cs.LGmath.OCstat.MLarXiv:2610.11475v12026
Automatic Posterior Transformation for Likelihood-Free Inference
David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke
cs.LGstat.MLarXiv:1905.07488v12019
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
cs.LGcs.AIcs.CVarXiv:2009.06962v22020
bartMachine: Machine Learning with Bayesian Additive Regression Trees
Adam Kapelner, Justin Bleich
stat.MLcs.LGarXiv:1312.2171v32013
A Priori Estimates of the Population Risk for Two-layer Neural Networks
Weinan E, Chao Ma, Lei Wu
stat.MLcs.LGmath.STarXiv:1810.06397v32018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Dongkun Zhang, Lu Lu, Ling Guo +1
math.APphysics.comp-phstat.MLarXiv:1809.08327v12018
Understanding and mitigating gradient pathologies in physics-informed neural networks
Sifan Wang, Yujun Teng, Paris Perdikaris
cs.LGmath.NAstat.MLarXiv:2001.04536v12020
Parametrized quantum policies for reinforcement learning
Sofiene Jerbi, Casper Gyurik, Simon C. Marshall +2
quant-phcs.AIcs.LGarXiv:2103.05577v22021
Continuous Diffusion Scales Competitively with Discrete Diffusion for Language
Zhihan Yang, Wei Guo, Shuibai Zhang +5
cs.CLcs.AIcs.LGarXiv:2605.18530v22026
Column Networks for Collective Classification
Trang Pham, Truyen Tran, Dinh Phung +1
cs.LGcs.AIstat.MLarXiv:1609.04508v22016
Private Stochastic Convex Optimization with Optimal Rates
Raef Bassily, Vitaly Feldman, Kunal Talwar +1
cs.LGcs.CRcs.DSarXiv:1908.09970v12019
Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms
Amirreza Rezvantalab, Habib Safigholi, Somayeh Karimijeshni
cs.CVcs.LGstat.MLarXiv:1810.10348v12018
Invariant Information Bottleneck for Domain Generalization
Bo Li, Yifei Shen, Yezhen Wang +6
cs.LGstat.MLarXiv:2106.06333v62021
A Finite Time Analysis of Two Time-Scale Actor Critic Methods
Yue Wu, Weitong Zhang, Pan Xu +1
cs.LGmath.OCstat.MLarXiv:2005.01350v32020
Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.5.6.5
Avanti Shrikumar, Katherine Tian, Žiga Avsec +5
cs.LGq-bio.GNstat.MLarXiv:1811.00416v52018
Orthogonal Statistical Learning
Dylan J. Foster, Vasilis Syrgkanis
math.STcs.LGecon.EMarXiv:1901.09036v42019
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan +2
stat.MLcs.LGmath.OCarXiv:1609.00978v12016
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
Hao Wang, Dit-Yan Yeung
stat.MLcs.CVcs.LGarXiv:1608.06884v22016
Learning Models with Uniform Performance via Distributionally Robust Optimization
John Duchi, Hongseok Namkoong
stat.MLcs.LGarXiv:1810.08750v62018
Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden, Joshua R. Wang
cs.DScs.GTcs.LGarXiv:1805.09480v12018
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
Tian Shi, Yaser Keneshloo, Naren Ramakrishnan +1
cs.CLcs.LGstat.MLarXiv:1812.02303v42018
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond
Xiao-Tong Yuan, Ping Li
stat.MLcs.LGarXiv:2206.05187v12022
Deep Learning Based Text Classification: A Comprehensive Review
Shervin Minaee, Nal Kalchbrenner, Erik Cambria +3
cs.CLcs.LGstat.MLarXiv:2004.03705v32020
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li +4
cs.LGcs.AIstat.MLarXiv:1910.12911v12019
Multi-level Residual Networks from Dynamical Systems View
Bo Chang, Lili Meng, Eldad Haber +2
stat.MLcs.CVarXiv:1710.10348v22017
Training Agents Inside of Scalable World Models
Danijar Hafner, Wilson Yan, Timothy Lillicrap
cs.AIcs.LGcs.ROarXiv:2509.24527v12025
Variational Graph Recurrent Neural Networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield +3
cs.LGstat.MLarXiv:1908.09710v32019

Machine Learning (stat)

Efficient Dataset Distillation Using Random Feature Approximation

Optimal Scaling Needs Optimal Norm

GRAND: Graph Neural Diffusion

Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

Bellman-consistent Pessimism for Offline Reinforcement Learning

Multiple Instance Learning with Bag Dissimilarities

Adversarial Regularizers in Inverse Problems

Latent mixed-effect models for high-dimensional longitudinal data

Deep Generative Adversarial Networks for Compressed Sensing Automates MRI

Conditional molecular design with deep generative models

Random sampling of bandlimited signals on graphs

Adaptive Attention Span in Transformers

Charting the Right Manifold: Manifold Mixup for Few-shot Learning

Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

Deep Generative Models for Distribution-Preserving Lossy Compression

Robust Reinforcement Learning for Continuous Control with Model Misspecification

Revisiting Classifier Two-Sample Tests

High Fidelity Neural Audio Compression

Polynomial-Chaos-based Kriging

Parseval Networks: Improving Robustness to Adversarial Examples

Deep Reinforcement Learning for Cyber Security

Deep Anomaly Detection Using Geometric Transformations

Optimal Clustering Framework for Hyperspectral Band Selection

Spectrally-normalized margin bounds for neural networks

Cascading Bandits: Learning to Rank in the Cascade Model

Proof-of-Learning: Definitions and Practice

BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs

Flow Matching under Noisy Latent Structure: Beyond Exact Low-Dimensional Support

Asymmetric Valleys: Beyond Sharp and Flat Local Minima

AMLB: an AutoML Benchmark

A Closed-Form Formula for Consistent Lipschitz Regression on Metric Spaces with Sparse Neural Network Realizations

The Principles of Deep Learning Theory

CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling

Rare Gate Disagreements Can Limit Plasticity: When Gradient Flow Mispredicts Finite-Batch SGD

Automatic Posterior Transformation for Likelihood-Free Inference

Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup

bartMachine: Machine Learning with Bayesian Additive Regression Trees

A Priori Estimates of the Population Risk for Two-layer Neural Networks

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

Understanding and mitigating gradient pathologies in physics-informed neural networks

Parametrized quantum policies for reinforcement learning

Continuous Diffusion Scales Competitively with Discrete Diffusion for Language

Column Networks for Collective Classification

Private Stochastic Convex Optimization with Optimal Rates

Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms

Invariant Information Bottleneck for Domain Generalization

A Finite Time Analysis of Two Time-Scale Actor Critic Methods

Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.5.6.5

Orthogonal Statistical Learning

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences

Towards Bayesian Deep Learning: A Framework and Some Existing Methods

Learning Models with Uniform Performance via Distributionally Robust Optimization

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond

Deep Learning Based Text Classification: A Comprehensive Review

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

Multi-level Residual Networks from Dynamical Systems View

Training Agents Inside of Scalable World Models

Variational Graph Recurrent Neural Networks