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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7,921 to 7,980 of 9,702

Pointer Networks
Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
stat.MLcs.CGcs.LGarXiv:1506.03134v22015
Parameter Space Noise for Exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal +6
cs.LGcs.AIcs.NEarXiv:1706.01905v22017
Multigrid with rough coefficients and Multiresolution operator decomposition from Hierarchical Information Games
Houman Owhadi
math.NAcs.AImath.STarXiv:1503.03467v52015
Objective Mismatch in Model-based Reinforcement Learning
Nathan Lambert, Brandon Amos, Omry Yadan +1
cs.LGcs.ROstat.MLarXiv:2002.04523v32020
Robust Federated Learning: The Case of Affine Distribution Shifts
Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani +1
cs.LGmath.OCstat.MLarXiv:2006.08907v12020
On the Equivalence of Stochastic Control and Path Space Formulations for Schr\"odinger Bridges over Compact Connected Lie Groups
Hamza Mahmood, Georgiy A. Bondar, Abhishek Halder +1
math.OCcs.AIcs.SYarXiv:2609.13758v12026
DSNAS: Direct Neural Architecture Search without Parameter Retraining
Shoukang Hu, Sirui Xie, Hehui Zheng +4
cs.LGstat.MLarXiv:2002.09128v22020
SCALOR: Generative World Models with Scalable Object Representations
Jindong Jiang, Sepehr Janghorbani, Gerard de Melo +1
cs.LGstat.MLarXiv:1910.02384v42019
Nonparametric Bayesian sparse factor models with application to gene expression modeling
David Knowles, Zoubin Ghahramani
stat.APcs.AIstat.MLarXiv:1011.6293v22010
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks
Wei Hu, Lechao Xiao, Jeffrey Pennington
cs.LGcs.NEmath.OCarXiv:2001.05992v12020
Improving GANs for Speech Enhancement
Huy Phan, Ian V. McLoughlin, Lam Pham +4
cs.LGcs.SDeess.ASarXiv:2001.05532v32020
Dimension-Corrected Hitting Times for Heavy-Tailed Spectral Emergence in Neural Optimizer Dynamics
Zongmin Liu
cs.LGcs.NEstat.MLarXiv:2609.12994v12026
Deep Gaussian Mixture Models
Cinzia Viroli, Geoffrey J. McLachlan
stat.MLcs.LGarXiv:1711.06929v12017
Certifiably Interpretable Training of ReLU-MLPs for Boolean Tasks with Guaranteed Truth-Table Generalization
Hrad Ghoukasian, Anastasis Kratsios
cs.LGcs.AIstat.MLarXiv:2609.13439v12026
Improving Variational Auto-Encoders using Householder Flow
Jakub M. Tomczak, Max Welling
cs.LGstat.MLarXiv:1611.09630v42016
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
E. Kharazmi, Z. Zhang, G. E. Karniadakis
cs.NEcs.LGmath.NAarXiv:1912.00873v12019
Approximate Data Deletion from Machine Learning Models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri +1
cs.LGstat.MLarXiv:2002.10077v22020
Understanding Self-Training for Gradual Domain Adaptation
Ananya Kumar, Tengyu Ma, Percy Liang
cs.LGstat.MLarXiv:2002.11361v12020
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning
Zhongjie Yu, Lin Chen, Zhongwei Cheng +1
cs.LGcs.CVstat.MLarXiv:1912.09033v22019
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values
Zhiyong Cui, Ruimin Ke, Ziyuan Pu +1
cs.LGeess.SPstat.MLarXiv:2005.11627v12020
Simplicial Closure and higher-order link prediction
Austin R. Benson, Rediet Abebe, Michael T. Schaub +2
cs.SIcond-mat.stat-mechmath.ATarXiv:1802.06916v22018
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Pin-Yu Chen, Yash Sharma, Huan Zhang +2
stat.MLcs.CRcs.LGarXiv:1709.04114v32017
Multi-Task Learning as Multi-Objective Optimization
Ozan Sener, Vladlen Koltun
cs.LGstat.MLarXiv:1810.04650v22018
On the Limitations of Unsupervised Bilingual Dictionary Induction
Anders Søgaard, Sebastian Ruder, Ivan Vulić
cs.CLcs.LGstat.MLarXiv:1805.03620v12018
Wide Compression: Tensor Ring Nets
Wenqi Wang, Yifan Sun, Brian Eriksson +2
cs.LGcs.CVstat.MLarXiv:1802.09052v12018
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma +1
cs.LGcs.AIstat.MLarXiv:1908.01000v32019
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization
Ben Adlam, Jeffrey Pennington
stat.MLcs.LGarXiv:2008.06786v12020
Causal Discovery with Continuous Additive Noise Models
Jonas Peters, Joris Mooij, Dominik Janzing +1
stat.MLarXiv:1309.6779v42013
Online Knowledge Distillation with Diverse Peers
Defang Chen, Jian-Ping Mei, Can Wang +2
cs.LGstat.MLarXiv:1912.00350v22019
Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification
Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette
cs.LGcs.CVstat.MLarXiv:1712.00032v22017
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Micah J Sheller, G Anthony Reina, Brandon Edwards +2
cs.LGstat.MLarXiv:1810.04304v22018
Automated Detection of Match Phases in Football from Spatio-Temporal Tracking Data Using Graph Neural Networks
Vincent Renner, Nils Koster, Pascal Bauer +1
stat.APstat.MLarXiv:2610.11571v12026
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
Denis Yarats, Amy Zhang, Ilya Kostrikov +3
cs.LGcs.AIcs.ROarXiv:1910.01741v32019
Distributed learning with regularized least squares
Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou
cs.LGstat.MLarXiv:1608.03339v22016
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization
Horia Mania, Xinghao Pan, Dimitris Papailiopoulos +3
stat.MLcs.DCcs.DSarXiv:1507.06970v22015
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
stat.MLcs.LGarXiv:2006.08545v12020
Neural Expectation Maximization
Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber
cs.LGcs.NEstat.MLarXiv:1708.03498v22017
A review on outlier/anomaly detection in time series data
Ane Blázquez-García, Angel Conde, Usue Mori +1
cs.LGstat.MLarXiv:2002.04236v12020
Mean Field Analysis of Neural Networks: A Central Limit Theorem
Justin Sirignano, Konstantinos Spiliopoulos
math.PRmath.STstat.MLarXiv:1808.09372v22018
FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things
Xiaying Wang, Michele Magno, Lukas Cavigelli +1
cs.LGeess.SPstat.MLarXiv:1911.03314v32019
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang +9
cs.SEcs.CRcs.LGarXiv:1803.07519v42018
Reachability Analysis of Deep Neural Networks with Provable Guarantees
Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska
cs.LGcs.CVstat.MLarXiv:1805.02242v12018
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu +1
stat.MLcs.CVcs.LGarXiv:1612.04052v12016
AutoML: A Survey of the State-of-the-Art
Xin He, Kaiyong Zhao, Xiaowen Chu
cs.LGcs.CVstat.MLarXiv:1908.00709v62019
Multi-task Neural Networks for QSAR Predictions
George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov
stat.MLcs.LGcs.NEarXiv:1406.1231v12014
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
Reinhard Heckel, Paul Hand
cs.CVcs.LGstat.MLarXiv:1810.03982v22018
Graph Convolutional Networks with EigenPooling
Yao Ma, Suhang Wang, Charu C. Aggarwal +1
cs.LGstat.MLarXiv:1904.13107v22019
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama +1
stat.MLcs.ITcs.LGarXiv:1905.13021v12019
Low Bit-Rate Speech Coding with VQ-VAE and a WaveNet Decoder
Cristina Gârbacea, Aäron van den Oord, Yazhe Li +4
cs.LGcs.SDeess.ASarXiv:1910.06464v12019
Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius
Junjie Jiang, Ying-Cheng Lai
cs.LGnlin.CDphysics.data-anarXiv:1910.04426v12019
The Geometry of Ignorance: LLMs Know When to Temper Bayesian Priors
Toni J. B. Liu, Jiajun Bao, Yizhou Liu +4
cs.LGcs.AIcs.CLarXiv:2609.02959v12026
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
Jie Zhou, Xu Han, Cheng Yang +4
cs.CLcs.AIcs.LGarXiv:1908.01843v12019
Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining
Rajeev Yasarla, Vishal M. Patel
cs.CVcs.LGeess.IVarXiv:1906.11129v12019
Federated Learning for Ultra-Reliable Low-Latency V2V Communications
Sumudu Samarakoon, Mehdi Bennis, Walid Saad +1
cs.NIcs.LGstat.MLarXiv:1805.09253v12018
ProxQuant: Quantized Neural Networks via Proximal Operators
Yu Bai, Yu-Xiang Wang, Edo Liberty
cs.LGstat.MLarXiv:1810.00861v32018
Learning Latent Space Energy-Based Prior Model
Bo Pang, Tian Han, Erik Nijkamp +2
stat.MLcs.LGarXiv:2006.08205v22020
Predicting Process Behaviour using Deep Learning
Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke
cs.LGstat.MLarXiv:1612.04600v22016
Provably Efficient Q-Learning with Low Switching Cost
Yu Bai, Tengyang Xie, Nan Jiang +1
cs.LGcs.AIstat.MLarXiv:1905.12849v32019
Temporal Network Embedding with Micro- and Macro-dynamics
Yuanfu Lu, Xiao Wang, Chuan Shi +2
cs.LGcs.SIstat.MLarXiv:1909.04246v12019
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Sanjeev Arora, Simon S. Du, Zhiyuan Li +3
cs.LGstat.MLarXiv:1910.01663v32019

Machine Learning (stat)

Pointer Networks

Parameter Space Noise for Exploration

Multigrid with rough coefficients and Multiresolution operator decomposition from Hierarchical Information Games

Objective Mismatch in Model-based Reinforcement Learning

Robust Federated Learning: The Case of Affine Distribution Shifts

On the Equivalence of Stochastic Control and Path Space Formulations for Schr\"odinger Bridges over Compact Connected Lie Groups

DSNAS: Direct Neural Architecture Search without Parameter Retraining

SCALOR: Generative World Models with Scalable Object Representations

Nonparametric Bayesian sparse factor models with application to gene expression modeling

Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks

Improving GANs for Speech Enhancement

Dimension-Corrected Hitting Times for Heavy-Tailed Spectral Emergence in Neural Optimizer Dynamics

Deep Gaussian Mixture Models

Certifiably Interpretable Training of ReLU-MLPs for Boolean Tasks with Guaranteed Truth-Table Generalization

Improving Variational Auto-Encoders using Householder Flow

Variational Physics-Informed Neural Networks For Solving Partial Differential Equations

Approximate Data Deletion from Machine Learning Models

Understanding Self-Training for Gradual Domain Adaptation

TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning

Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values

Simplicial Closure and higher-order link prediction

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Multi-Task Learning as Multi-Objective Optimization

On the Limitations of Unsupervised Bilingual Dictionary Induction

Wide Compression: Tensor Ring Nets

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization

Causal Discovery with Continuous Additive Noise Models

Online Knowledge Distillation with Diverse Peers

Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification

Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

Automated Detection of Match Phases in Football from Spatio-Temporal Tracking Data Using Graph Neural Networks

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Distributed learning with regularized least squares

Perturbed Iterate Analysis for Asynchronous Stochastic Optimization

Why Normalizing Flows Fail to Detect Out-of-Distribution Data

Neural Expectation Maximization

A review on outlier/anomaly detection in time series data

Mean Field Analysis of Neural Networks: A Central Limit Theorem

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Reachability Analysis of Deep Neural Networks with Provable Guarantees

Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

AutoML: A Survey of the State-of-the-Art

Multi-task Neural Networks for QSAR Predictions

Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks

Graph Convolutional Networks with EigenPooling

Robustness to Adversarial Perturbations in Learning from Incomplete Data

Low Bit-Rate Speech Coding with VQ-VAE and a WaveNet Decoder

Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius

The Geometry of Ignorance: LLMs Know When to Temper Bayesian Priors

GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification

Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

ProxQuant: Quantized Neural Networks via Proximal Operators

Learning Latent Space Energy-Based Prior Model

Predicting Process Behaviour using Deep Learning

Provably Efficient Q-Learning with Low Switching Cost

Temporal Network Embedding with Micro- and Macro-dynamics

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks