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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7,381 to 7,440 of 9,715

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
Measures of Entropy from Data Using Infinitely Divisible Kernels
Luis G. Sanchez Giraldo, Murali Rao, Jose C. Principe
cs.LGcs.ITstat.MLarXiv:1211.2459v32012
Verified Uncertainty Calibration
Ananya Kumar, Percy Liang, Tengyu Ma
cs.LGstat.MLarXiv:1909.10155v22019
Denoising Adversarial Autoencoders
Antonia Creswell, Anil Anthony Bharath
cs.CVcs.LGstat.MLarXiv:1703.01220v42017
Visualizing Data using GTSNE
Songting Shi
cs.LGcs.HCmath.OCarXiv:2108.01301v12021
The CLRS Algorithmic Reasoning Benchmark
Petar Veličković, Adrià Puigdomènech Badia, David Budden +5
cs.LGcs.DSstat.MLarXiv:2205.15659v22022
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg +5
cs.LGphysics.chem-phstat.MLarXiv:1703.00564v32017
Collaborative Nested Sampling: Big Data vs. complex physical models
Johannes Buchner
stat.COastro-ph.IMphysics.data-anarXiv:1707.04476v52017
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste
cs.LGcs.AIcs.CVarXiv:1805.10123v42018
Max-value Entropy Search for Efficient Bayesian Optimization
Zi Wang, Stefanie Jegelka
stat.MLcs.LGmath.OCarXiv:1703.01968v32017
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets
Frederik Kratzert, Daniel Klotz, Guy Shalev +3
cs.LGstat.MLarXiv:1907.08456v22019
Kernel Mean Embedding of Distributions: A Review and Beyond
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur +1
stat.MLcs.LGarXiv:1605.09522v42016
Contrastive Self-supervised Learning for Graph Classification
Jiaqi Zeng, Pengtao Xie
cs.LGstat.MLarXiv:2009.05923v12020

Machine Learning (stat)

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

Measures of Entropy from Data Using Infinitely Divisible Kernels

Verified Uncertainty Calibration

Denoising Adversarial Autoencoders

Visualizing Data using GTSNE

The CLRS Algorithmic Reasoning Benchmark

MoleculeNet: A Benchmark for Molecular Machine Learning

Collaborative Nested Sampling: Big Data vs. complex physical models

TADAM: Task dependent adaptive metric for improved few-shot learning

Max-value Entropy Search for Efficient Bayesian Optimization

Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets

Kernel Mean Embedding of Distributions: A Review and Beyond

Contrastive Self-supervised Learning for Graph Classification