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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5,161 to 5,220 of 6,784

Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
Muhammad Ghifary, David Balduzzi, W. Bastiaan Kleijn +1
cs.CVcs.AIcs.LGarXiv:1510.04373v22015
Model Reduction and Neural Networks for Parametric PDEs
Kaushik Bhattacharya, Bamdad Hosseini, Nikola B. Kovachki +1
math.NAcs.LGstat.MLarXiv:2005.03180v22020
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
Yunzhu Li, Jiajun Wu, Russ Tedrake +2
cs.LGcs.AIcs.ROarXiv:1810.01566v22018
Characterizing Implicit Bias in Terms of Optimization Geometry
Suriya Gunasekar, Jason Lee, Daniel Soudry +1
stat.MLcs.LGarXiv:1802.08246v32018
Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Ines Chami, Adva Wolf, Da-Cheng Juan +3
cs.LGcs.AIcs.CLarXiv:2005.00545v12020
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis
Rong Ge, Chi Jin, Yi Zheng
cs.LGmath.OCstat.MLarXiv:1704.00708v12017
High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski, Nicholas Carlini, David Berthelot +2
cs.LGcs.CRstat.MLarXiv:1909.01838v22019
Max-value Entropy Search for Efficient Bayesian Optimization
Zi Wang, Stefanie Jegelka
stat.MLcs.LGmath.OCarXiv:1703.01968v32017
Hyperparameter Search in Machine Learning
Marc Claesen, Bart De Moor
cs.LGstat.MLarXiv:1502.02127v22015
Implicit Bias of Gradient Descent on Linear Convolutional Networks
Suriya Gunasekar, Jason Lee, Daniel Soudry +1
cs.LGstat.MLarXiv:1806.00468v22018
SIGN: Scalable Inception Graph Neural Networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard +3
cs.LGstat.MLarXiv:2004.11198v32020
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Shang-Tse Chen, Cory Cornelius, Jason Martin +1
cs.CVcs.CRcs.LGarXiv:1804.05810v32018
Measuring the Effects of Data Parallelism on Neural Network Training
Christopher J. Shallue, Jaehoon Lee, Joseph Antognini +3
cs.LGstat.MLarXiv:1811.03600v32018
SGD: General Analysis and Improved Rates
Robert Mansel Gower, Nicolas Loizou, Xun Qian +3
cs.LGmath.OCstat.MLarXiv:1901.09401v42019
Analyzing the Structure of Attention in a Transformer Language Model
Jesse Vig, Yonatan Belinkov
cs.CLcs.LGstat.MLarXiv:1906.04284v22019
Algorithms for Verifying Deep Neural Networks
Changliu Liu, Tomer Arnon, Christopher Lazarus +3
cs.LGstat.MLarXiv:1903.06758v22019
Lower Bounds for Non-Convex Stochastic Optimization
Yossi Arjevani, Yair Carmon, John C. Duchi +3
math.OCcs.ITcs.LGarXiv:1912.02365v22019
Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar +7
cs.LGq-bio.QMstat.MLarXiv:1706.01643v12017
Benchmarking Multivariate Time Series Classification Algorithms
Alejandro Pasos Ruiz, Michael Flynn, Anthony Bagnall
cs.LGstat.MLarXiv:2007.13156v22020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
Qiang Huang, Makoto Yamada, Yuan Tian +3
cs.LGstat.MLarXiv:2001.06216v22020
MaskGAN: Better Text Generation via Filling in the______
William Fedus, Ian Goodfellow, Andrew M. Dai
stat.MLcs.AIcs.LGarXiv:1801.07736v32018
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
Song Liu, Makoto Yamada, Nigel Collier +1
stat.MLcs.LGstat.MEarXiv:1203.0453v22012
Towards Deep Conversational Recommendations
Raymond Li, Samira Kahou, Hannes Schulz +3
cs.LGcs.CLcs.IRarXiv:1812.07617v22018
Automated Algorithm Selection: Survey and Perspectives
Pascal Kerschke, Holger H. Hoos, Frank Neumann +1
cs.LGcs.AIstat.MLarXiv:1811.11597v12018
Machine Learning of coarse-grained Molecular Dynamics Force Fields
Jiang Wang, Simon Olsson, Christoph Wehmeyer +5
physics.comp-phcs.LGstat.MLarXiv:1812.01736v32018
Problems with Shapley-value-based explanations as feature importance measures
I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger +1
cs.AIcs.LGstat.MLarXiv:2002.11097v22020
Generalized Zero-Shot Learning via Synthesized Examples
Vinay Kumar Verma, Gundeep Arora, Ashish Mishra +1
cs.LGcs.CVstat.MLarXiv:1712.03878v52017
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow
cs.LGstat.MLarXiv:2006.04884v32020
Learning Deep Representations with Probabilistic Knowledge Transfer
Nikolaos Passalis, Anastasios Tefas
cs.LGcs.NEstat.MLarXiv:1803.10837v32018
FACE: Feasible and Actionable Counterfactual Explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez +2
cs.LGstat.MLarXiv:1909.09369v22019
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
cs.LGstat.MLarXiv:1906.01227v22019
Generative Language Modeling for Automated Theorem Proving
Stanislas Polu, Ilya Sutskever
cs.LGcs.AIcs.CLarXiv:2009.03393v12020
InfoVAE: Information Maximizing Variational Autoencoders
Shengjia Zhao, Jiaming Song, Stefano Ermon
cs.LGcs.AIstat.MLarXiv:1706.02262v32017
The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan +2
cs.LGcs.AIstat.MLarXiv:2006.07710v22020
On the Expressive Power of Deep Learning: A Tensor Analysis
Nadav Cohen, Or Sharir, Amnon Shashua
cs.NEcs.LGmath.NAarXiv:1509.05009v32015
AudioPaLM: A Large Language Model That Can Speak and Listen
Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen +27
cs.CLcs.AIcs.SDarXiv:2306.12925v12023
Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto, H. Francis Song, Jack W. Rae +10
cs.LGcs.AIstat.MLarXiv:1910.06764v12019
TextWorld: A Learning Environment for Text-based Games
Marc-Alexandre Côté, Ákos Kádár, Xingdi Yuan +10
cs.LGcs.CLstat.MLarXiv:1806.11532v22018
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
Giuseppe Ateniese, Giovanni Felici, Luigi V. Mancini +3
cs.CRcs.LGstat.MLarXiv:1306.4447v12013
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
stat.MLcs.LGarXiv:1810.11953v42018
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos, Max Welling
stat.MLcs.LGarXiv:1703.01961v22017
Optimizing Millions of Hyperparameters by Implicit Differentiation
Jonathan Lorraine, Paul Vicol, David Duvenaud
cs.LGstat.MLarXiv:1911.02590v12019
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
cs.LGcs.CVstat.MLarXiv:2204.02937v22022
A Survey on Graph Kernels
Nils M. Kriege, Fredrik D. Johansson, Christopher Morris
cs.LGstat.MLarXiv:1903.11835v22019
No More Pesky Learning Rates
Tom Schaul, Sixin Zhang, Yann LeCun
stat.MLcs.LGarXiv:1206.1106v22012
Adversarially Regularized Graph Autoencoder for Graph Embedding
Shirui Pan, Ruiqi Hu, Guodong Long +3
cs.LGstat.MLarXiv:1802.04407v22018
Convolutional neural networks with low-rank regularization
Cheng Tai, Tong Xiao, Yi Zhang +2
cs.LGcs.CVstat.MLarXiv:1511.06067v32015
Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman, Andrew Ilyas, Logan Engstrom +2
cs.CVcs.LGstat.MLarXiv:2007.08489v22020
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Georgios Kissas, Yibo Yang, Eileen Hwuang +3
cs.LGstat.MLarXiv:1905.04817v22019
Deep Parametric Continuous Convolutional Neural Networks
Shenlong Wang, Simon Suo, Wei-Chiu Ma +2
cs.CVcs.AIcs.LGarXiv:2101.06742v12021
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Mi Luo, Fei Chen, Dapeng Hu +3
cs.LGcs.CVcs.DCarXiv:2106.05001v22021
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Tony Duan, Anand Avati, Daisy Yi Ding +4
cs.LGstat.MLarXiv:1910.03225v42019
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Borja Balle, Gilles Barthe, Marco Gaboardi
cs.LGcs.CRstat.MLarXiv:1807.01647v22018
Explaining Neural Scaling Laws
Yasaman Bahri, Ethan Dyer, Jared Kaplan +2
cs.LGcond-mat.dis-nnstat.MLarXiv:2102.06701v22021
Dynamics-Aware Unsupervised Discovery of Skills
Archit Sharma, Shixiang Gu, Sergey Levine +2
cs.LGcs.ROstat.MLarXiv:1907.01657v22019
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
Characterizing Full Nonequilibrium Dynamics of Simple Exclusion Processes
Zhimao Liu, Jing Liu, Pan Zhang +1
cond-mat.stat-mechcond-mat.dis-nnstat.MLarXiv:2608.25606v12026
On Tiny Episodic Memories in Continual Learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny +4
cs.LGstat.MLarXiv:1902.10486v42019
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk +3
cs.LGstat.MLarXiv:1912.04871v42019
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Gregory Farquhar, Bei Peng +1
cs.LGcs.MAstat.MLarXiv:2006.10800v22020

Machine Learning (stat)

Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization

Model Reduction and Neural Networks for Parametric PDEs

Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Characterizing Implicit Bias in Terms of Optimization Geometry

Low-Dimensional Hyperbolic Knowledge Graph Embeddings

No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis

High Accuracy and High Fidelity Extraction of Neural Networks

Max-value Entropy Search for Efficient Bayesian Optimization

Hyperparameter Search in Machine Learning

Implicit Bias of Gradient Descent on Linear Convolutional Networks

SIGN: Scalable Inception Graph Neural Networks

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

Measuring the Effects of Data Parallelism on Neural Network Training

SGD: General Analysis and Improved Rates

Analyzing the Structure of Attention in a Transformer Language Model

Algorithms for Verifying Deep Neural Networks

Lower Bounds for Non-Convex Stochastic Optimization

Retrosynthetic reaction prediction using neural sequence-to-sequence models

Benchmarking Multivariate Time Series Classification Algorithms

GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

MaskGAN: Better Text Generation via Filling in the______

Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation

Towards Deep Conversational Recommendations

Automated Algorithm Selection: Survey and Perspectives

Machine Learning of coarse-grained Molecular Dynamics Force Fields

Problems with Shapley-value-based explanations as feature importance measures

Generalized Zero-Shot Learning via Synthesized Examples

On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines

Learning Deep Representations with Probabilistic Knowledge Transfer

FACE: Feasible and Actionable Counterfactual Explanations

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

Generative Language Modeling for Automated Theorem Proving

InfoVAE: Information Maximizing Variational Autoencoders

The Pitfalls of Simplicity Bias in Neural Networks

On the Expressive Power of Deep Learning: A Tensor Analysis

AudioPaLM: A Large Language Model That Can Speak and Listen

Stabilizing Transformers for Reinforcement Learning

TextWorld: A Learning Environment for Text-based Games

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

Multiplicative Normalizing Flows for Variational Bayesian Neural Networks

Optimizing Millions of Hyperparameters by Implicit Differentiation

Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations

A Survey on Graph Kernels

No More Pesky Learning Rates

Adversarially Regularized Graph Autoencoder for Graph Embedding

Convolutional neural networks with low-rank regularization

Do Adversarially Robust ImageNet Models Transfer Better?

Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

Deep Parametric Continuous Convolutional Neural Networks

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

Explaining Neural Scaling Laws

Dynamics-Aware Unsupervised Discovery of Skills

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

Characterizing Full Nonequilibrium Dynamics of Simple Exclusion Processes

On Tiny Episodic Memories in Continual Learning

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning