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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5,281 to 5,340 of 6,786

Schrödinger Bridges over Kinetic Swarming Models
Asmaa Eldesoukey, Md Zulfiqur Haider, Italo Napolitano +2
math.OCeess.SYmath-pharXiv:2608.25281v12026
Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang, Kun Xu, Chao Du +2
cs.LGstat.MLarXiv:1901.08846v32019
Data Augmentation for Graph Neural Networks
Tong Zhao, Yozen Liu, Leonardo Neves +3
cs.LGstat.MLarXiv:2006.06830v22020
Learning by Playing - Solving Sparse Reward Tasks from Scratch
Martin Riedmiller, Roland Hafner, Thomas Lampe +6
cs.LGcs.ROstat.MLarXiv:1802.10567v12018
Sensitivity and Generalization in Neural Networks: an Empirical Study
Roman Novak, Yasaman Bahri, Daniel A. Abolafia +2
stat.MLcs.AIcs.LGarXiv:1802.08760v32018
Stealing Hyperparameters in Machine Learning
Binghui Wang, Neil Zhenqiang Gong
cs.CRcs.LGstat.MLarXiv:1802.05351v32018
WHAM!: Extending Speech Separation to Noisy Environments
Gordon Wichern, Joe Antognini, Michael Flynn +5
cs.SDcs.CLcs.LGarXiv:1907.01160v12019
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
Anshumali Shrivastava, Ping Li
stat.MLcs.DScs.IRarXiv:1405.5869v12014
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application
Te Han, Chao Liu, Wenguang Yang +1
cs.LGstat.MLarXiv:1804.07265v12018
Learning to Optimize Tensor Programs
Tianqi Chen, Lianmin Zheng, Eddie Yan +5
cs.LGstat.MLarXiv:1805.08166v42018
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
George C. Linderman, Manas Rachh, Jeremy G. Hoskins +2
cs.LGstat.MLarXiv:1712.09005v12017
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Jonathan Ho, Xi Chen, Aravind Srinivas +2
cs.LGcs.NEstat.MLarXiv:1902.00275v22019
Adapting Neural Networks for the Estimation of Treatment Effects
Claudia Shi, David M. Blei, Victor Veitch
stat.MLcs.LGstat.MEarXiv:1906.02120v22019
Multimodal Generative Models for Scalable Weakly-Supervised Learning
Mike Wu, Noah Goodman
cs.LGstat.MLarXiv:1802.05335v32018
Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks
Xiaoyuan Liang, Xunsheng Du, Guiling Wang +1
cs.LGcs.AIstat.MLarXiv:1803.11115v12018
Learning Activation Functions to Improve Deep Neural Networks
Forest Agostinelli, Matthew Hoffman, Peter Sadowski +1
cs.NEcs.CVcs.LGarXiv:1412.6830v32014
Machine Learning at the Network Edge: A Survey
M. G. Sarwar Murshed, Christopher Murphy, Daqing Hou +3
cs.LGcs.CVcs.NIarXiv:1908.00080v42019
Systematic Evaluation of Privacy Risks of Machine Learning Models
Liwei Song, Prateek Mittal
cs.CRcs.LGstat.MLarXiv:2003.10595v22020
SpotTune: Transfer Learning through Adaptive Fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar +3
cs.CVcs.LGstat.MLarXiv:1811.08737v12018
Shampoo: Preconditioned Stochastic Tensor Optimization
Vineet Gupta, Tomer Koren, Yoram Singer
cs.LGmath.OCstat.MLarXiv:1802.09568v22018
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov +3
cs.LGcs.CRcs.DSarXiv:1811.12469v22018
Representation Tradeoffs for Hyperbolic Embeddings
Christopher De Sa, Albert Gu, Christopher Ré +1
cs.LGstat.MLarXiv:1804.03329v22018
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt +1
cs.LGstat.MLarXiv:1805.09076v22018
Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning
Ruiqi Zhang, Licong Lin, Yu Bai +1
cs.LGcs.AIcs.CLarXiv:2404.05868v22024
Planning to Explore via Self-Supervised World Models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis +3
cs.LGcs.AIcs.CVarXiv:2005.05960v22020
Composing graphical models with neural networks for structured representations and fast inference
Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko +2
stat.MLarXiv:1603.06277v52016
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Dimitrios Kollias, Panagiotis Tzirakis, Mihalis A. Nicolaou +5
cs.CVcs.AIcs.HCarXiv:1804.10938v52018
Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik +8
q-bio.MNcs.LGq-bio.QMarXiv:2004.07229v22020
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
Chen-Yu Lee, Patrick W. Gallagher, Zhuowen Tu
stat.MLcs.LGcs.NEarXiv:1509.08985v22015
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen +1
stat.MLcs.LGmath.OCarXiv:1908.08729v22019
Disentangling factors of variation in deep representations using adversarial training
Michael Mathieu, Junbo Zhao, Pablo Sprechmann +2
cs.LGstat.MLarXiv:1611.03383v12016
On Neural Differential Equations
Patrick Kidger
cs.LGmath.CAmath.DSarXiv:2202.02435v12022
Monte Carlo Gradient Estimation in Machine Learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov +1
stat.MLcs.LGmath.OCarXiv:1906.10652v22019
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen +5
stat.MLcs.CRcs.LGarXiv:1801.10578v12018
Top2Vec: Distributed Representations of Topics
Dimo Angelov
cs.CLcs.LGstat.MLarXiv:2008.09470v12020
Revisiting Multiple Instance Neural Networks
Xinggang Wang, Yongluan Yan, Peng Tang +2
stat.MLcs.LGarXiv:1610.02501v12016
Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Chao Li, Zhiyuan Liu, Mengmeng Wu +7
cs.IRcs.LGstat.MLarXiv:1904.08030v12019
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman, Ran El-Yaniv
cs.LGstat.MLarXiv:1901.09192v42019
Optimal whitening and decorrelation
Agnan Kessy, Alex Lewin, Korbinian Strimmer
stat.MEstat.MLarXiv:1512.00809v42015
Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
Leslie N. Smith, Nicholay Topin
cs.LGcs.CVcs.NEarXiv:1708.07120v32017
Characterizing and Avoiding Negative Transfer
Zirui Wang, Zihang Dai, Barnabás Póczos +1
cs.LGstat.MLarXiv:1811.09751v42018
ECG Heartbeat Classification: A Deep Transferable Representation
Mohammad Kachuee, Shayan Fazeli, Majid Sarrafzadeh
cs.CYcs.LGstat.MLarXiv:1805.00794v22018
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
Denis Yarats, Amy Zhang, Ilya Kostrikov +3
cs.LGcs.AIcs.ROarXiv:1910.01741v32019
Analysing Mathematical Reasoning Abilities of Neural Models
David Saxton, Edward Grefenstette, Felix Hill +1
cs.LGstat.MLarXiv:1904.01557v12019
Differentially Private Fine-tuning of Language Models
Da Yu, Saurabh Naik, Arturs Backurs +9
cs.LGcs.CLcs.CRarXiv:2110.06500v22021
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra
cs.LGcs.AIstat.MLarXiv:2304.02858v32023
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
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
Zheng Li, Yue Zhao, Xiyang Hu +3
cs.LGcs.DBstat.AParXiv:2201.00382v32022
Provable Non-Acceleration of Standard Strang Splittings of Kinetic Langevin Dynamics
Nawaf Bou-Rabee
math.PRmath.NAstat.COarXiv:2608.25279v12026
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
Guillaume Jaume, Hazim Kemal Ekenel, Jean-Philippe Thiran
cs.IRcs.CVcs.LGarXiv:1905.13538v22019
DiffTaichi: Differentiable Programming for Physical Simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li +4
cs.LGcs.GRphysics.comp-pharXiv:1910.00935v32019
Accelerated Hierarchical Density Clustering
Leland McInnes, John Healy
stat.MLarXiv:1705.07321v22017
A Stochastic Quasi-Newton Method for Large-Scale Optimization
R. H. Byrd, S. L. Hansen, J. Nocedal +1
math.OCcs.LGstat.MLarXiv:1401.7020v22014
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
Kaihua Tang, Jianqiang Huang, Hanwang Zhang
cs.CVcs.LGstat.MLarXiv:2009.12991v52020
Dense Associative Memory for Pattern Recognition
Dmitry Krotov, John J Hopfield
cs.NEcond-mat.dis-nncs.LGarXiv:1606.01164v22016
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Hoang NT, Takanori Maehara
stat.MLcs.ITcs.LGarXiv:1905.09550v22019
A System for Massively Parallel Hyperparameter Tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh +4
cs.LGstat.MLarXiv:1810.05934v52018
Google Research Football: A Novel Reinforcement Learning Environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk +8
cs.LGstat.MLarXiv:1907.11180v22019
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Sebastian Caldas, Jakub Konečny, H. Brendan McMahan +1
cs.LGcs.DCstat.MLarXiv:1812.07210v22018
To Trust Or Not To Trust A Classifier
Heinrich Jiang, Been Kim, Melody Y. Guan +1
stat.MLcs.LGarXiv:1805.11783v22018

Machine Learning (stat)

Schrödinger Bridges over Kinetic Swarming Models

Improving Adversarial Robustness via Promoting Ensemble Diversity

Data Augmentation for Graph Neural Networks

Learning by Playing - Solving Sparse Reward Tasks from Scratch

Sensitivity and Generalization in Neural Networks: an Empirical Study

Stealing Hyperparameters in Machine Learning

WHAM!: Extending Speech Separation to Noisy Environments

Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)

Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application

Learning to Optimize Tensor Programs

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Adapting Neural Networks for the Estimation of Treatment Effects

Multimodal Generative Models for Scalable Weakly-Supervised Learning

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

Learning Activation Functions to Improve Deep Neural Networks

Machine Learning at the Network Edge: A Survey

Systematic Evaluation of Privacy Risks of Machine Learning Models

SpotTune: Transfer Learning through Adaptive Fine-tuning

Shampoo: Preconditioned Stochastic Tensor Optimization

Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

Representation Tradeoffs for Hyperbolic Embeddings

Constrained Graph Variational Autoencoders for Molecule Design

Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning

Planning to Explore via Self-Supervised World Models

Composing graphical models with neural networks for structured representations and fast inference

Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Disentangling factors of variation in deep representations using adversarial training

On Neural Differential Equations

Monte Carlo Gradient Estimation in Machine Learning

Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

Top2Vec: Distributed Representations of Topics

Revisiting Multiple Instance Neural Networks

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

Optimal whitening and decorrelation

Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

Characterizing and Avoiding Negative Transfer

ECG Heartbeat Classification: A Deep Transferable Representation

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Analysing Mathematical Reasoning Abilities of Neural Models

Differentially Private Fine-tuning of Language Models

A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation

Fast inference of deep neural networks in FPGAs for particle physics

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

Provable Non-Acceleration of Standard Strang Splittings of Kinetic Langevin Dynamics

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

DiffTaichi: Differentiable Programming for Physical Simulation

Accelerated Hierarchical Density Clustering

A Stochastic Quasi-Newton Method for Large-Scale Optimization

Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect

Dense Associative Memory for Pattern Recognition

Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

A System for Massively Parallel Hyperparameter Tuning

Google Research Football: A Novel Reinforcement Learning Environment

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

To Trust Or Not To Trust A Classifier