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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5,221 to 5,280 of 6,786

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
Neural Autoregressive Flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste +1
cs.LGstat.MLarXiv:1804.00779v12018
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Weihua Hu, Takeru Miyato, Seiya Tokui +2
stat.MLcs.LGarXiv:1702.08720v32017
Continuous Time Dynamic Topic Models
Chong Wang, David Blei, David Heckerman
cs.IRcs.LGstat.MLarXiv:1206.3298v22012
The Evolved Transformer
David R. So, Chen Liang, Quoc V. Le
cs.LGcs.CLcs.NEarXiv:1901.11117v42019
Learning to Detect
Neev Samuel, Tzvi Diskin, Ami Wiesel
cs.ITcs.LGstat.MLarXiv:1805.07631v12018
Parallel Coordinate Descent Methods for Big Data Optimization
Peter Richtárik, Martin Takáč
math.OCcs.AIstat.MLarXiv:1212.0873v22012
L2 Regularization for Learning Kernels
Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
cs.LGstat.MLarXiv:1205.2653v12012
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization
Thomas Desautels, Andreas Krause, Joel Burdick
cs.LGstat.MLarXiv:1206.6402v12012
Distributed Robust Power System State Estimation
Vassilis Kekatos, Georgios B. Giannakis
stat.MLmath.OCarXiv:1204.0991v22012
Elliptical slice sampling
Iain Murray, Ryan Prescott Adams, David J. C. MacKay
stat.COstat.MLarXiv:1001.0175v22009
Finding Density Functionals with Machine Learning
John C. Snyder, Matthias Rupp, Katja Hansen +2
physics.comp-phcs.LGphysics.chem-pharXiv:1112.5441v12011
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection
Abhimanyu Das, David Kempe
stat.MLcs.DSarXiv:1102.3975v22011
Petuum: A New Platform for Distributed Machine Learning on Big Data
Eric P. Xing, Qirong Ho, Wei Dai +7
stat.MLcs.LGeess.SYarXiv:1312.7651v22013
Scaled Sparse Linear Regression
Tingni Sun, Cun-Hui Zhang
stat.MLmath.STarXiv:1104.4595v22011
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
Han Liu, Kathryn Roeder, Larry Wasserman
stat.MLarXiv:1006.3316v12010
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
Yuejie Chi, Yue M. Lu, Yuxin Chen
cs.LGcs.ITeess.SParXiv:1809.09573v32018
Massively Multitask Networks for Drug Discovery
Bharath Ramsundar, Steven Kearnes, Patrick Riley +3
stat.MLcs.LGcs.NEarXiv:1502.02072v12015
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl +4
cs.LGcs.CRstat.MLarXiv:1811.04017v22018
Group Sparse Regularization for Deep Neural Networks
Simone Scardapane, Danilo Comminiello, Amir Hussain +1
stat.MLcs.LGarXiv:1607.00485v12016
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
Michael Lutter, Christian Ritter, Jan Peters
cs.LGcs.ROeess.SYarXiv:1907.04490v12019
Performative Prediction
Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner +1
cs.LGcs.GTstat.MLarXiv:2002.06673v42020
On the Computational Efficiency of Training Neural Networks
Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
cs.LGcs.AIstat.MLarXiv:1410.1141v22014
Generalization in Deep Learning
Kenji Kawaguchi, Leslie Pack Kaelbling, Yoshua Bengio
stat.MLcs.AIcs.LGarXiv:1710.05468v92017
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
Abhishek Das, Harsh Agrawal, C. Lawrence Zitnick +2
stat.MLcs.CVarXiv:1606.05589v12016
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Florian Tramèr, Dan Boneh
stat.MLcs.CRcs.LGarXiv:1806.03287v22018
A General Framework for Metropolis-Adjusted Dikin Walks: Dimension-Square Mixing on Polytopes and Log-Det Walks on Spectrahedra
Zhao Song, Lichen Zhang
cs.DSstat.MLarXiv:2608.25273v12026
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Yi Liu, Sahil Garg, Jiangtian Nie +4
cs.LGcs.DCstat.MLarXiv:2007.09712v12020
Reward learning from human preferences and demonstrations in Atari
Borja Ibarz, Jan Leike, Tobias Pohlen +3
cs.LGcs.AIcs.NEarXiv:1811.06521v12018
Hyperbolic Graph Neural Networks
Qi Liu, Maximilian Nickel, Douwe Kiela
cs.LGstat.MLarXiv:1910.12892v12019
CREPE: A Convolutional Representation for Pitch Estimation
Jong Wook Kim, Justin Salamon, Peter Li +1
eess.AScs.LGcs.SDarXiv:1802.06182v12018
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Yuzhe Yang, Zhi Xu
cs.LGcs.CVstat.MLarXiv:2006.07529v22020
Learning to Walk via Deep Reinforcement Learning
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou +3
cs.LGcs.AIcs.ROarXiv:1812.11103v32018
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
cs.LGstat.MLarXiv:1905.12322v32019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks
Ruth Fong, Mandela Patrick, Andrea Vedaldi
cs.CVcs.LGstat.MLarXiv:1910.08485v12019
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Yu-Xiang Wang, Borja Balle, Shiva Kasiviswanathan
cs.LGcs.CRstat.MLarXiv:1808.00087v22018
Empirical Risk Minimization under Fairness Constraints
Michele Donini, Luca Oneto, Shai Ben-David +2
stat.MLcs.LGarXiv:1802.08626v32018
Uncertainty Sets for Image Classifiers using Conformal Prediction
Anastasios Angelopoulos, Stephen Bates, Jitendra Malik +1
cs.CVmath.STstat.MLarXiv:2009.14193v52020
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
Hao Wu, Patrick Judd, Xiaojie Zhang +2
cs.LGstat.MLarXiv:2004.09602v12020
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Hao Yuan, Jiliang Tang, Xia Hu +1
cs.LGstat.MLarXiv:2006.02587v12020
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
Alessio Benavoli, Giorgio Corani, Janez Demsar +1
stat.MLcs.LGarXiv:1606.04316v32016
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
Guangneng Hu, Yu Zhang, Qiang Yang
cs.IRcs.AIcs.LGarXiv:1804.06769v32018
Deep learning for undersampled MRI reconstruction
Chang Min Hyun, Hwa Pyung Kim, Sung Min Lee +2
stat.MLcs.LGphysics.med-pharXiv:1709.02576v32017
DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback
David Burth Kurka, Deniz Gündüz
cs.ITcs.LGeess.IVarXiv:1911.11174v22019
Graph Neural Networks with convolutional ARMA filters
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi +1
cs.LGstat.MLarXiv:1901.01343v72019
Statistical physics of inference: Thresholds and algorithms
Lenka Zdeborová, Florent Krzakala
cond-mat.stat-mechcs.DSstat.MLarXiv:1511.02476v52015
Neural Architecture Search without Training
Joseph Mellor, Jack Turner, Amos Storkey +1
cs.LGcs.CVstat.MLarXiv:2006.04647v32020
Neural Collaborative Filtering vs. Matrix Factorization Revisited
Steffen Rendle, Walid Krichene, Li Zhang +1
cs.IRcs.LGstat.MLarXiv:2005.09683v22020
Selection via Proxy: Efficient Data Selection for Deep Learning
Cody Coleman, Christopher Yeh, Stephen Mussmann +5
cs.LGstat.MLarXiv:1906.11829v42019
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Derek Lim, Felix Hohne, Xiuyu Li +4
cs.LGcs.SIstat.MLarXiv:2110.14446v12021
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
Alex Olsen, Dmitry A. Konovalov, Bronson Philippa +10
cs.CVcs.LGstat.MLarXiv:1810.05726v32018
DDSP: Differentiable Digital Signal Processing
Jesse Engel, Lamtharn Hantrakul, Chenjie Gu +1
cs.LGcs.SDeess.ASarXiv:2001.04643v12020
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
Zhanqiu Zhang, Jianyu Cai, Yongdong Zhang +1
cs.LGcs.CLstat.MLarXiv:1911.09419v32019
Bayesian Compression for Deep Learning
Christos Louizos, Karen Ullrich, Max Welling
stat.MLcs.LGarXiv:1705.08665v42017
CoverNet: Multimodal Behavior Prediction using Trajectory Sets
Tung Phan-Minh, Elena Corina Grigore, Freddy A. Boulton +2
cs.LGcs.ROstat.MLarXiv:1911.10298v22019
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner +2
cs.LGstat.MLarXiv:1706.08500v62017
Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias Khalil +3
cs.LGcs.DScs.NEarXiv:2102.09544v32021
Discovering Phase Transitions with Unsupervised Learning
Lei Wang
cond-mat.stat-mechstat.MLarXiv:1606.00318v22016
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Aapo Hyvarinen, Hiroshi Morioka
stat.MLcs.LGarXiv:1605.06336v12016

Machine Learning (stat)

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

Neural Autoregressive Flows

Learning Discrete Representations via Information Maximizing Self-Augmented Training

Continuous Time Dynamic Topic Models

The Evolved Transformer

Learning to Detect

Parallel Coordinate Descent Methods for Big Data Optimization

L2 Regularization for Learning Kernels

Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization

Distributed Robust Power System State Estimation

Elliptical slice sampling

Finding Density Functionals with Machine Learning

Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection

Petuum: A New Platform for Distributed Machine Learning on Big Data

Scaled Sparse Linear Regression

Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

Massively Multitask Networks for Drug Discovery

A generic framework for privacy preserving deep learning

Group Sparse Regularization for Deep Neural Networks

Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Performative Prediction

On the Computational Efficiency of Training Neural Networks

Generalization in Deep Learning

Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?

Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware

A General Framework for Metropolis-Adjusted Dikin Walks: Dimension-Square Mixing on Polytopes and Log-Det Walks on Spectrahedra

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

Reward learning from human preferences and demonstrations in Atari

Hyperbolic Graph Neural Networks

CREPE: A Convolutional Representation for Pitch Estimation

Rethinking the Value of Labels for Improving Class-Imbalanced Learning

Learning to Walk via Deep Reinforcement Learning

A Study of BFLOAT16 for Deep Learning Training

Understanding Deep Networks via Extremal Perturbations and Smooth Masks

Subsampled Rényi Differential Privacy and Analytical Moments Accountant

Empirical Risk Minimization under Fairness Constraints

Uncertainty Sets for Image Classifiers using Conformal Prediction

Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

XGNN: Towards Model-Level Explanations of Graph Neural Networks

Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis

CoNet: Collaborative Cross Networks for Cross-Domain Recommendation

Deep learning for undersampled MRI reconstruction

DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback

Graph Neural Networks with convolutional ARMA filters

Statistical physics of inference: Thresholds and algorithms

Neural Architecture Search without Training

Neural Collaborative Filtering vs. Matrix Factorization Revisited

Selection via Proxy: Efficient Data Selection for Deep Learning

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning

DDSP: Differentiable Digital Signal Processing

Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

Bayesian Compression for Deep Learning

CoverNet: Multimodal Behavior Prediction using Trajectory Sets

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Combinatorial optimization and reasoning with graph neural networks

Discovering Phase Transitions with Unsupervised Learning

Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA