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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4,621 to 4,680 of 6,787

TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
cs.LGcs.PFstat.MLarXiv:2001.09249v12020
Transformers Can Do Bayesian Inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango +2
cs.LGstat.MLarXiv:2112.10510v72021
Gradient descent GAN optimization is locally stable
Vaishnavh Nagarajan, J. Zico Kolter
cs.LGcs.AImath.OCarXiv:1706.04156v32017
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde +6
cs.LGcs.AIstat.MLarXiv:2305.02997v42023
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
Wei-Ning Hsu, Yu Zhang, James Glass
cs.LGcs.CLcs.SDarXiv:1709.07902v12017
Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist +2
cs.LGstat.MLarXiv:1711.07839v12017
Recurrent Neural Networks For Accurate RSSI Indoor Localization
Minh Tu Hoang, Brosnan Yuen, Xiaodai Dong +3
eess.SPcs.LGstat.MLarXiv:1903.11703v22019
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Keyulu Xu, Mozhi Zhang, Jingling Li +3
cs.LGcs.AIcs.CVarXiv:2009.11848v52020
iNNvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer +7
cs.LGstat.MLarXiv:1808.04260v12018
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent +5
stat.MLcs.AIcs.LGarXiv:1706.00400v22017
The Risks of Invariant Risk Minimization
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
cs.LGcs.AIstat.MLarXiv:2010.05761v22020
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
Tongwen Huang, Zhiqi Zhang, Junlin Zhang
cs.LGcs.AIstat.MLarXiv:1905.09433v12019
Neural networks for the prediction organic chemistry reactions
Jennifer N. Wei, David Duvenaud, Alán Aspuru-Guzik
physics.chem-phq-bio.QMstat.MLarXiv:1608.06296v22016
PolyGen: An Autoregressive Generative Model of 3D Meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami +1
cs.GRcs.CVcs.LGarXiv:2002.10880v12020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Sven Gowal, Chongli Qin, Jonathan Uesato +2
stat.MLcs.AIcs.LGarXiv:2010.03593v32020
Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
Péter Mernyei, Cătălina Cangea
cs.LGcs.SIstat.MLarXiv:2007.02901v22020
FACMAC: Factored Multi-Agent Centralised Policy Gradients
Bei Peng, Tabish Rashid, Christian A. Schroeder de Witt +4
cs.LGcs.AIstat.MLarXiv:2003.06709v52020
Bayesian Nonparametric Hidden Semi-Markov Models
Matthew J. Johnson, Alan S. Willsky
stat.MEstat.APstat.MLarXiv:1203.1365v22012
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Ofir Nachum, Yinlam Chow, Bo Dai +1
cs.LGcs.AIstat.MLarXiv:1906.04733v22019
Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, Su-In Lee
cs.LGstat.MLarXiv:2011.14878v22020
Variance-based regularization with convex objectives
John Duchi, Hongseok Namkoong
stat.MLmath.STarXiv:1610.02581v32016
Discrete Distribution Estimation under Local Privacy
Peter Kairouz, Keith Bonawitz, Daniel Ramage
stat.MLcs.LGarXiv:1602.07387v32016
CERT: Contrastive Self-supervised Learning for Language Understanding
Hongchao Fang, Sicheng Wang, Meng Zhou +2
cs.CLcs.LGstat.MLarXiv:2005.12766v22020
A Note on Over-Smoothing for Graph Neural Networks
Chen Cai, Yusu Wang
cs.LGstat.MLarXiv:2006.13318v12020
State Representation Learning for Control: An Overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou +1
cs.AIcs.LGstat.MLarXiv:1802.04181v22018
MeshCNN: A Network with an Edge
Rana Hanocka, Amir Hertz, Noa Fish +3
cs.LGcs.CVcs.GRarXiv:1809.05910v22018
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting
Jiawei Zhu, Yujiao Song, Ling Zhao +1
cs.LGstat.MLarXiv:2006.11583v12020
A Convex Framework for Fair Regression
Richard Berk, Hoda Heidari, Shahin Jabbari +5
cs.LGstat.MLarXiv:1706.02409v12017
Scalable Methods for 8-bit Training of Neural Networks
Ron Banner, Itay Hubara, Elad Hoffer +1
cs.LGstat.MLarXiv:1805.11046v32018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize
Tengyuan Liang, Alexander Rakhlin
math.STcs.LGstat.MLarXiv:1808.00387v22018
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh, Been Kim, Sercan O. Arik +3
cs.LGstat.MLarXiv:1910.07969v62019
Sparse Networks from Scratch: Faster Training without Losing Performance
Tim Dettmers, Luke Zettlemoyer
cs.LGcs.NEstat.MLarXiv:1907.04840v22019
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
Hao Zhang, Zeyu Zheng, Shizhen Xu +7
cs.LGcs.CVcs.DCarXiv:1706.03292v12017
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Junyuan Shang, Cao Xiao, Tengfei Ma +2
cs.AIcs.LGstat.MLarXiv:1809.01852v32018
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács +6
stat.MLcond-mat.mtrl-scics.LGarXiv:2205.06643v22022
Meta-Gradient Reinforcement Learning
Zhongwen Xu, Hado van Hasselt, David Silver
cs.LGcs.AIstat.MLarXiv:1805.09801v12018
Concept Whitening for Interpretable Image Recognition
Zhi Chen, Yijie Bei, Cynthia Rudin
cs.LGcs.AIcs.CVarXiv:2002.01650v52020
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
John Duchi, Peter Glynn, Hongseok Namkoong
stat.MLarXiv:1610.03425v32016
Program Evaluation and Causal Inference with High-Dimensional Data
Alexandre Belloni, Victor Chernozhukov, Ivan Fernández-Val +1
math.STecon.EMstat.MEarXiv:1311.2645v82013
Recent Advances in Convolutional Neural Network Acceleration
Qianru Zhang, Meng Zhang, Tinghuan Chen +3
cs.LGstat.MLarXiv:1807.08596v12018
Compacting, Picking and Growing for Unforgetting Continual Learning
Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu +3
cs.LGstat.MLarXiv:1910.06562v32019
Task2Vec: Task Embedding for Meta-Learning
Alessandro Achille, Michael Lam, Rahul Tewari +5
cs.LGcs.AIstat.MLarXiv:1902.03545v12019
Negative Margin Matters: Understanding Margin in Few-shot Classification
Bin Liu, Yue Cao, Yutong Lin +4
cs.CVcs.LGstat.MLarXiv:2003.12060v12020
Approximate Data Deletion from Machine Learning Models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri +1
cs.LGstat.MLarXiv:2002.10077v22020
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
cs.CVcs.LGstat.MLarXiv:1802.04034v32018
Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen, Leslie O'Bray, Karsten Borgwardt
stat.MLcs.LGarXiv:2202.03036v32022
PacGAN: The power of two samples in generative adversarial networks
Zinan Lin, Ashish Khetan, Giulia Fanti +1
cs.LGcs.ITstat.MLarXiv:1712.04086v32017
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
Minhao Cheng, Thong Le, Pin-Yu Chen +3
cs.LGcs.AIstat.MLarXiv:1807.04457v12018
Addressing Failure Prediction by Learning Model Confidence
Charles Corbière, Nicolas Thome, Avner Bar-Hen +2
cs.CVcs.LGstat.MLarXiv:1910.04851v22019
Dataset Distillation
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba +1
cs.LGstat.MLarXiv:1811.10959v32018
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
Yoshua Bengio, Tristan Deleu, Nasim Rahaman +5
cs.LGstat.MLarXiv:1901.10912v22019
Feature relevance quantification in explainable AI: A causal problem
Dominik Janzing, Lenon Minorics, Patrick Blöbaum
stat.MLcs.LGarXiv:1910.13413v22019
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Leila Arras, Grégoire Montavon, Klaus-Robert Müller +1
cs.CLcs.AIcs.NEarXiv:1706.07206v22017
Distributed Stochastic Gradient Tracking Methods
Shi Pu, Angelia Nedić
math.OCcs.DCcs.SIarXiv:1805.11454v52018
Towards better decoding and language model integration in sequence to sequence models
Jan Chorowski, Navdeep Jaitly
cs.NEcs.CLcs.LGarXiv:1612.02695v12016
On the Apparent Conflict Between Individual and Group Fairness
Reuben Binns
cs.LGcs.CYstat.MLarXiv:1912.06883v12019
A unifying view for performance measures in multi-class prediction
Giuseppe Jurman, Cesare Furlanello
stat.MLarXiv:1008.2908v12010
FairGAN: Fairness-aware Generative Adversarial Networks
Depeng Xu, Shuhan Yuan, Lu Zhang +1
cs.LGcs.CYstat.MLarXiv:1805.11202v12018
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks
Ying Zhang, Mohammad Pezeshki, Philemon Brakel +3
cs.CLcs.LGstat.MLarXiv:1701.02720v12017
Information Leakage in Embedding Models
Congzheng Song, Ananth Raghunathan
cs.LGcs.CLcs.CRarXiv:2004.00053v22020

Machine Learning (stat)

TiFL: A Tier-based Federated Learning System

Transformers Can Do Bayesian Inference

Gradient descent GAN optimization is locally stable

When Do Neural Nets Outperform Boosted Trees on Tabular Data?

Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

Application of generative autoencoder in de novo molecular design

Recurrent Neural Networks For Accurate RSSI Indoor Localization

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

iNNvestigate neural networks!

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

The Risks of Invariant Risk Minimization

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

Neural networks for the prediction organic chemistry reactions

PolyGen: An Autoregressive Generative Model of 3D Meshes

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

FACMAC: Factored Multi-Agent Centralised Policy Gradients

Bayesian Nonparametric Hidden Semi-Markov Models

DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections

Explaining by Removing: A Unified Framework for Model Explanation

Variance-based regularization with convex objectives

Discrete Distribution Estimation under Local Privacy

CERT: Contrastive Self-supervised Learning for Language Understanding

A Note on Over-Smoothing for Graph Neural Networks

State Representation Learning for Control: An Overview

MeshCNN: A Network with an Edge

A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

A Convex Framework for Fair Regression

Scalable Methods for 8-bit Training of Neural Networks

Just Interpolate: Kernel "Ridgeless" Regression Can Generalize

On Completeness-aware Concept-Based Explanations in Deep Neural Networks

Sparse Networks from Scratch: Faster Training without Losing Performance

Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

Meta-Gradient Reinforcement Learning

Concept Whitening for Interpretable Image Recognition

Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach

Program Evaluation and Causal Inference with High-Dimensional Data

Recent Advances in Convolutional Neural Network Acceleration

Compacting, Picking and Growing for Unforgetting Continual Learning

Task2Vec: Task Embedding for Meta-Learning

Negative Margin Matters: Understanding Margin in Few-shot Classification

Approximate Data Deletion from Machine Learning Models

Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks

Structure-Aware Transformer for Graph Representation Learning

PacGAN: The power of two samples in generative adversarial networks

Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach

Addressing Failure Prediction by Learning Model Confidence

Dataset Distillation

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

Feature relevance quantification in explainable AI: A causal problem

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Distributed Stochastic Gradient Tracking Methods

Towards better decoding and language model integration in sequence to sequence models

On the Apparent Conflict Between Individual and Group Fairness

A unifying view for performance measures in multi-class prediction

FairGAN: Fairness-aware Generative Adversarial Networks

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

Information Leakage in Embedding Models