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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2,041 to 2,100 of 6,790

A deep learning model for estimating story points
Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran +3
cs.SEcs.LGstat.MLarXiv:1609.00489v22016
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Ziwei Ji, Matus Telgarsky
cs.LGmath.OCstat.MLarXiv:1909.12292v42019
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
Kimin Lee, Kibok Lee, Jinwoo Shin +1
cs.LGstat.MLarXiv:1910.05396v32019
CEM-RL: Combining evolutionary and gradient-based methods for policy search
Aloïs Pourchot, Olivier Sigaud
cs.LGcs.NEstat.MLarXiv:1810.01222v32018
When Many Answers Are Valid, Voting Fails: Symbolic Verification for Best-of-K Causal Reasoning in LLMs
Omatharv Bharat Vaidya, Connor Thomas Jerzak, Zayne Rea Sprague +2
cs.AIstat.MLarXiv:2608.03506v12026
Neural Networks Provably Learn Spectral Representations for Group Composition
Jianliang He, Leda Wang, Fengzhuo Zhang +2
cs.LGmath.OCmath.RTarXiv:2606.02993v22026
Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation
Samson Gourevitch, Yazid Janati, Dario Shariatian +4
cs.LGstat.MLarXiv:2605.22765v12026
SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction
Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov, Hafize Gonca Cömert
cs.LGecon.EMstat.MLarXiv:2605.19014v12026
From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion
Satoshi Hayakawa
cs.LGcs.AImath.PRarXiv:2609.01043v12026
Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD
Emiel Hoogeboom, David Ruhe, Jonathan Heek +2
cs.LGcs.CVstat.MLarXiv:2603.20155v12026
Remasking Discrete Diffusion Models with Inference-Time Scaling
Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo +1
cs.LGstat.MLarXiv:2503.00307v42025
SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
Shahriar Noroozizadeh, Xiaobin Shen, Jeremy C. Weiss +1
cs.LGcs.AIstat.MLarXiv:2603.05483v12026
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
Alekh Agarwal, Sham Kakade, Lin F. Yang
cs.LGmath.PRstat.MLarXiv:1906.03804v32019
On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking
Jianliang He, Leda Wang, Siyu Chen +1
cs.LGmath.OCstat.MLarXiv:2602.16849v12026
Introduction to Machine Learning
Laurent Younes
stat.MLcs.LGarXiv:2409.02668v22024
Momentum Improves Normalized SGD
Ashok Cutkosky, Harsh Mehta
cs.LGmath.OCstat.MLarXiv:2002.03305v22020
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
Vitaly Feldman, Audra McMillan, Kunal Talwar
cs.LGcs.CRcs.DSarXiv:2012.12803v32020
Towards Understanding Sycophancy in Language Models
Mrinank Sharma, Meg Tong, Tomasz Korbak +16
cs.CLcs.AIcs.LGarXiv:2310.13548v42023
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao +6
cs.LGcs.AIcs.CVarXiv:2310.02279v32023
Consistency Models
Yang Song, Prafulla Dhariwal, Mark Chen +1
cs.LGcs.CVstat.MLarXiv:2303.01469v22023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin +6
cs.LGcs.CRstat.MLarXiv:2303.00654v32023
Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
Fernando Gama, Elvin Isufi, Geert Leus +1
cs.LGeess.SYstat.MLarXiv:2003.03777v52020
An efficient EM algorithm for both element-wise and structural missingness in matrix-variate normal mixture models
Hanzhang Lu, Jeffrey L. Andrews, Ryan P. Browne
stat.MEstat.COstat.MLarXiv:2609.00616v12026
Classifier Calibration: A survey on how to assess and improve predicted class probabilities
Telmo Silva Filho, Hao Song, Miquel Perello-Nieto +3
cs.LGstat.MLarXiv:2112.10327v22021
User-friendly introduction to PAC-Bayes bounds
Pierre Alquier
stat.MLcs.LGmath.STarXiv:2110.11216v62021
Frame Averaging for Invariant and Equivariant Network Design
Omri Puny, Matan Atzmon, Heli Ben-Hamu +4
cs.LGstat.MLarXiv:2110.03336v42021
Variational Diffusion Models
Diederik P. Kingma, Tim Salimans, Ben Poole +1
cs.LGstat.MLarXiv:2107.00630v62021
The Principles of Deep Learning Theory
Daniel A. Roberts, Sho Yaida, Boris Hanin
cs.LGcs.AIhep-tharXiv:2106.10165v22021
Laplace Redux -- Effortless Bayesian Deep Learning
Erik Daxberger, Agustinus Kristiadi, Alexander Immer +3
cs.LGstat.MLarXiv:2106.14806v32021
Bellman-consistent Pessimism for Offline Reinforcement Learning
Tengyang Xie, Ching-An Cheng, Nan Jiang +2
cs.LGcs.AIstat.MLarXiv:2106.06926v62021
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Paria Rashidinejad, Banghua Zhu, Cong Ma +2
cs.LGcs.AImath.OCarXiv:2103.12021v22021
Generative hypergraph clustering: from blockmodels to modularity
Philip S. Chodrow, Nate Veldt, Austin R. Benson
cs.SIcs.DMphysics.data-anarXiv:2101.09611v42021
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Zoë Holmes, Kunal Sharma, M. Cerezo +1
quant-phcs.LGstat.MLarXiv:2101.02138v22021
Deep Compressed Sensing
Yan Wu, Mihaela Rosca, Timothy Lillicrap
cs.LGeess.SPstat.MLarXiv:1905.06723v22019
How Does Mixup Help With Robustness and Generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi +2
cs.LGstat.MLarXiv:2010.04819v42020
The Surprising Power of Graph Neural Networks with Random Node Initialization
Ralph Abboud, İsmail İlkan Ceylan, Martin Grohe +1
cs.LGcs.AIstat.MLarXiv:2010.01179v22020
Multivariate Time-series Anomaly Detection via Graph Attention Network
Hang Zhao, Yujing Wang, Juanyong Duan +7
cs.LGstat.MLarXiv:2009.02040v12020
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Hong-You Chen, Wei-Lun Chao
cs.LGstat.MLarXiv:2009.01974v42020
Dynamical Variational Autoencoders: A Comprehensive Review
Laurent Girin, Simon Leglaive, Xiaoyu Bie +3
cs.LGstat.MLarXiv:2008.12595v42020
Learning from Noisy Labels with Deep Neural Networks: A Survey
Hwanjun Song, Minseok Kim, Dongmin Park +2
cs.LGcs.CVstat.MLarXiv:2007.08199v72020
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
Robin M. Schmidt, Frank Schneider, Philipp Hennig
cs.LGstat.MLarXiv:2007.01547v62020
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
Afshin Shoeibi, Marjane Khodatars, Navid Ghassemi +14
cs.LGeess.SPstat.MLarXiv:2007.01276v32020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel, Jasper Snoek, Dustin Tran +1
cs.LGstat.MLarXiv:2006.13570v32020
When Do Neural Networks Outperform Kernel Methods?
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz +1
stat.MLcs.LGmath.STarXiv:2006.13409v22020
Sparse GPU Kernels for Deep Learning
Trevor Gale, Matei Zaharia, Cliff Young +1
cs.LGcs.DCstat.MLarXiv:2006.10901v22020
Deep Learning Meets SAR
Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali +6
eess.IVcs.LGstat.MLarXiv:2006.10027v22020
The prospects of quantum computing in computational molecular biology
Carlos Outeiral, Martin Strahm, Jiye Shi +3
quant-phq-bio.BMq-bio.GNarXiv:2005.12792v12020
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Joakim Skarding, Bogdan Gabrys, Katarzyna Musial
cs.SIcs.LGstat.MLarXiv:2005.07496v22020
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik +5
cs.LGcs.SIstat.MLarXiv:2005.00687v72020
Time Series Forecasting With Deep Learning: A Survey
Bryan Lim, Stefan Zohren
stat.MLcs.LGarXiv:2004.13408v22020
Deep Learning Based Text Classification: A Comprehensive Review
Shervin Minaee, Nal Kalchbrenner, Erik Cambria +3
cs.CLcs.LGstat.MLarXiv:2004.03705v32020
A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network
Seyoung Kim, Kyung-Ah Sohn, Eric P. Xing
stat.MLq-bio.GNq-bio.MNarXiv:0811.2026v12008
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Jinhyun So, Basak Guler, A. Salman Avestimehr
cs.LGcs.CRcs.DCarXiv:2002.04156v32020
Can Graph Neural Networks Count Substructures?
Zhengdao Chen, Lei Chen, Soledad Villar +1
cs.LGcs.DMstat.MLarXiv:2002.04025v42020
Naive Exploration is Optimal for Online LQR
Max Simchowitz, Dylan J. Foster
cs.LGmath.OCstat.MLarXiv:2001.09576v42020
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan +7
cs.LGstat.MLarXiv:2001.08361v12020
A CNN-RNN Framework for Crop Yield Prediction
Saeed Khaki, Lizhi Wang, Sotirios V. Archontoulis
cs.LGq-bio.QMstat.MLarXiv:1911.09045v22019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts +6
cs.LGcs.CLstat.MLarXiv:1910.10683v42019
Energy Efficient Federated Learning Over Wireless Communication Networks
Zhaohui Yang, Mingzhe Chen, Walid Saad +2
cs.ITcs.LGstat.MLarXiv:1911.02417v22019
Model Pruning Enables Efficient Federated Learning on Edge Devices
Yuang Jiang, Shiqiang Wang, Victor Valls +4
cs.LGcs.DCstat.MLarXiv:1909.12326v52019

Machine Learning (stat)

A deep learning model for estimating story points

Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

CEM-RL: Combining evolutionary and gradient-based methods for policy search

When Many Answers Are Valid, Voting Fails: Symbolic Verification for Best-of-K Causal Reasoning in LLMs

Neural Networks Provably Learn Spectral Representations for Group Composition

Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation

SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction

From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion

Beyond Single Tokens: Distilling Discrete Diffusion Models via Discrete MMD

Remasking Discrete Diffusion Models with Inference-Time Scaling

SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis

Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal

On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking

Introduction to Machine Learning

Momentum Improves Normalized SGD

Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling

Towards Understanding Sycophancy in Language Models

Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

Consistency Models

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks

An efficient EM algorithm for both element-wise and structural missingness in matrix-variate normal mixture models

Classifier Calibration: A survey on how to assess and improve predicted class probabilities

User-friendly introduction to PAC-Bayes bounds

Frame Averaging for Invariant and Equivariant Network Design

Variational Diffusion Models

The Principles of Deep Learning Theory

Laplace Redux -- Effortless Bayesian Deep Learning

Bellman-consistent Pessimism for Offline Reinforcement Learning

Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism

Generative hypergraph clustering: from blockmodels to modularity

Connecting ansatz expressibility to gradient magnitudes and barren plateaus

Deep Compressed Sensing

How Does Mixup Help With Robustness and Generalization?

The Surprising Power of Graph Neural Networks with Random Node Initialization

Multivariate Time-series Anomaly Detection via Graph Attention Network

FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning

Dynamical Variational Autoencoders: A Comprehensive Review

Learning from Noisy Labels with Deep Neural Networks: A Survey

Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

When Do Neural Networks Outperform Kernel Methods?

Sparse GPU Kernels for Deep Learning

Deep Learning Meets SAR

The prospects of quantum computing in computational molecular biology

Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey

Open Graph Benchmark: Datasets for Machine Learning on Graphs

Time Series Forecasting With Deep Learning: A Survey

Deep Learning Based Text Classification: A Comprehensive Review

A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

Can Graph Neural Networks Count Substructures?

Naive Exploration is Optimal for Online LQR

Scaling Laws for Neural Language Models

A CNN-RNN Framework for Crop Yield Prediction

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Energy Efficient Federated Learning Over Wireless Communication Networks

Model Pruning Enables Efficient Federated Learning on Edge Devices