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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3,841 to 3,900 of 6,790

Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Itay Safran, Ohad Shamir
cs.LGstat.MLarXiv:1712.08968v32017
Adaptive Aggregation Networks for Class-Incremental Learning
Yaoyao Liu, Bernt Schiele, Qianru Sun
cs.CVstat.MLarXiv:2010.05063v32020
Inductive Matrix Completion Based on Graph Neural Networks
Muhan Zhang, Yixin Chen
cs.IRcs.LGstat.MLarXiv:1904.12058v32019
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler
eess.IVcs.LGstat.MLarXiv:1904.02816v32019
What Can Neural Networks Reason About?
Keyulu Xu, Jingling Li, Mozhi Zhang +3
cs.LGcs.AIcs.CVarXiv:1905.13211v42019
Benchmarking Simulation-Based Inference
Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg +2
stat.MLcs.LGarXiv:2101.04653v22021
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung +4
cs.LGcs.CVq-bio.NCarXiv:1802.08195v32018
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball
eess.SPcs.LGq-bio.NCarXiv:1806.01875v12018
Differentiable Causal Discovery from Interventional Data
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste +2
cs.LGstat.MLarXiv:2007.01754v22020
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
Julien Brajard, Alberto Carassi, Marc Bocquet +1
stat.MLcs.LGphysics.ao-pharXiv:2001.01520v22020
Multi-task Neural Networks for QSAR Predictions
George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov
stat.MLcs.LGcs.NEarXiv:1406.1231v12014
Deep $k$-Means: Jointly clustering with $k$-Means and learning representations
Maziar Moradi Fard, Thibaut Thonet, Eric Gaussier
cs.LGstat.MLarXiv:1806.10069v22018
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
Bum Chul Kwon, Min-Je Choi, Joanne Taery Kim +5
cs.LGcs.HCstat.MLarXiv:1805.10724v32018
Transfer Learning for Non-Intrusive Load Monitoring
Michele DIncecco, Stefano Squartini, Mingjun Zhong
cs.LGstat.MLarXiv:1902.08835v32019
Transformers Learn Shortcuts to Automata
Bingbin Liu, Jordan T. Ash, Surbhi Goel +2
cs.LGcs.FLstat.MLarXiv:2210.10749v22022
Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast with Algebraic Optimal Step Size
Vicente Zarzoso, Pierre Comon
stat.MLarXiv:1002.3684v12010
Deep Convolutional Networks as shallow Gaussian Processes
Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison
stat.MLcs.LGarXiv:1808.05587v22018
Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
Fan Zhou, Chengtai Cao, Kunpeng Zhang +3
cs.LGstat.MLarXiv:1905.09718v12019
Stronger Data Poisoning Attacks Break Data Sanitization Defenses
Pang Wei Koh, Jacob Steinhardt, Percy Liang
stat.MLcs.CRcs.LGarXiv:1811.00741v22018
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk +9
cs.LGstat.MLarXiv:2006.05990v12020
Riemannian Score-Based Generative Modelling
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson +3
cs.LGmath.PRstat.MLarXiv:2202.02763v32022
Relational Pooling for Graph Representations
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao +1
cs.LGstat.MLarXiv:1903.02541v22019
Conditional Image Generation with Score-Based Diffusion Models
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb +1
cs.LGcs.CVstat.MLarXiv:2111.13606v12021
Ridge Regression, Hubness, and Zero-Shot Learning
Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara +2
cs.LGstat.MLarXiv:1507.00825v12015
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
Maziar Raissi, Paris Perdikaris, George Em Karniadakis
math.DSmath.NAnlin.CDarXiv:1801.01236v12018
Reachability Analysis of Deep Neural Networks with Provable Guarantees
Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska
cs.LGcs.CVstat.MLarXiv:1805.02242v12018
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples
Thomas Tanay, Lewis Griffin
cs.LGstat.MLarXiv:1608.07690v12016
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
Tim Dockhorn, Arash Vahdat, Karsten Kreis
stat.MLcs.LGarXiv:2112.07068v42021
On the properties of variational approximations of Gibbs posteriors
Pierre Alquier, James Ridgway, Nicolas Chopin
stat.MLmath.STarXiv:1506.04091v22015
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
Ignavier Ng, AmirEmad Ghassami, Kun Zhang
cs.LGstat.MLarXiv:2006.10201v32020
Hyperparameter Importance Across Datasets
J. N. van Rijn, F. Hutter
stat.MLcs.LGarXiv:1710.04725v22017
A review on distance based time series classification
Amaia Abanda, Usue Mori, Jose A. Lozano
stat.MLcs.LGarXiv:1806.04509v12018
End-to-End Robotic Reinforcement Learning without Reward Engineering
Avi Singh, Larry Yang, Kristian Hartikainen +2
cs.LGcs.CVcs.ROarXiv:1904.07854v22019
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Lior Hirschfeld, Kyle Swanson, Kevin Yang +2
cs.LGq-bio.QMstat.MLarXiv:2005.10036v12020
UVeQFed: Universal Vector Quantization for Federated Learning
Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar +2
cs.LGcs.ITstat.MLarXiv:2006.03262v32020
A Geometric Analysis of Neural Collapse with Unconstrained Features
Zhihui Zhu, Tianyu Ding, Jinxin Zhou +4
cs.LGcs.AIcs.ITarXiv:2105.02375v12021
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka +1
cs.SDcs.LGeess.ASarXiv:1904.04631v12019
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Yang Liu, Hongyi Guo
cs.LGstat.MLarXiv:1910.03231v72019
Efficient Exploration via State Marginal Matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto +3
cs.LGcs.AIcs.ROarXiv:1906.05274v32019
A Primer on the Signature Method in Machine Learning
Ilya Chevyrev, Andrey Kormilitzin
stat.MLcs.LGstat.MEarXiv:1603.03788v22016
Learning to Detect Malicious Clients for Robust Federated Learning
Suyi Li, Yong Cheng, Wei Wang +2
cs.LGcs.CRstat.MLarXiv:2002.00211v12020
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
Shuai Xiao, Junchi Yan, Stephen M. Chu +2
cs.LGcs.AIstat.MLarXiv:1705.08982v12017
Capsules for Object Segmentation
Rodney LaLonde, Ulas Bagci
stat.MLcs.AIcs.CVarXiv:1804.04241v12018
Meta-Learning Probabilistic Inference For Prediction
Jonathan Gordon, John Bronskill, Matthias Bauer +2
stat.MLcs.LGarXiv:1805.09921v42018
Med-HALT: Medical Domain Hallucination Test for Large Language Models
Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu
cs.CLcs.AIcs.LGarXiv:2307.15343v22023
Towards Sparse Hierarchical Graph Classifiers
Cătălina Cangea, Petar Veličković, Nikola Jovanović +2
stat.MLcs.AIcs.LGarXiv:1811.01287v12018
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Dimitrios Stamoulis, Ruizhou Ding, Di Wang +4
cs.LGcs.CVstat.MLarXiv:1904.02877v12019
Multi-graph Fusion for Multi-view Spectral Clustering
Zhao Kang, Guoxin Shi, Shudong Huang +4
cs.LGcs.CVstat.MLarXiv:1909.06940v12019
Label Efficient Learning of Transferable Representations across Domains and Tasks
Zelun Luo, Yuliang Zou, Judy Hoffman +1
stat.MLcs.CVarXiv:1712.00123v12017
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Esteban Real, Chen Liang, David R. So +1
cs.LGcs.NEstat.MLarXiv:2003.03384v22020
Discrete Variational Autoencoders
Jason Tyler Rolfe
stat.MLcs.LGarXiv:1609.02200v22016
Why is Posterior Sampling Better than Optimism for Reinforcement Learning?
Ian Osband, Benjamin Van Roy
stat.MLcs.AIcs.LGarXiv:1607.00215v32016
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data
Xinwei Zhang, Mingyi Hong, Sairaj Dhople +2
cs.LGstat.MLarXiv:2005.11418v32020
Near-Optimal Algorithms for Minimax Optimization
Tianyi Lin, Chi Jin, Michael. I. Jordan
math.OCcs.LGstat.MLarXiv:2002.02417v62020
Statistical inference on random dot product graphs: a survey
Avanti Athreya, Donniell E. Fishkind, Keith Levin +7
stat.MEmath.STstat.MLarXiv:1709.05454v12017
Splitting Methods for Convex Clustering
Eric C. Chi, Kenneth Lange
stat.MLmath.NAmath.OCarXiv:1304.0499v22013
Adversarial examples for generative models
Jernej Kos, Ian Fischer, Dawn Song
stat.MLcs.LGarXiv:1702.06832v12017
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Simon S. Du, Jason D. Lee
cs.LGcs.AImath.OCarXiv:1803.01206v22018
Self-Distillation Amplifies Regularization in Hilbert Space
Hossein Mobahi, Mehrdad Farajtabar, Peter L. Bartlett
cs.LGstat.MLarXiv:2002.05715v32020
Learning Stochastic Recurrent Networks
Justin Bayer, Christian Osendorfer
stat.MLcs.LGarXiv:1411.7610v32014

Machine Learning (stat)

Spurious Local Minima are Common in Two-Layer ReLU Neural Networks

Adaptive Aggregation Networks for Class-Incremental Learning

Inductive Matrix Completion Based on Graph Neural Networks

Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning

What Can Neural Networks Reason About?

Benchmarking Simulation-Based Inference

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals

Differentiable Causal Discovery from Interventional Data

Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model

Multi-task Neural Networks for QSAR Predictions

Deep $k$-Means: Jointly clustering with $k$-Means and learning representations

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records

Transfer Learning for Non-Intrusive Load Monitoring

Transformers Learn Shortcuts to Automata

Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast with Algebraic Optimal Step Size

Deep Convolutional Networks as shallow Gaussian Processes

Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

Stronger Data Poisoning Attacks Break Data Sanitization Defenses

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

Riemannian Score-Based Generative Modelling

Relational Pooling for Graph Representations

Conditional Image Generation with Score-Based Diffusion Models

Ridge Regression, Hubness, and Zero-Shot Learning

Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

Reachability Analysis of Deep Neural Networks with Provable Guarantees

A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples

Score-Based Generative Modeling with Critically-Damped Langevin Diffusion

On the properties of variational approximations of Gibbs posteriors

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

Hyperparameter Importance Across Datasets

A review on distance based time series classification

End-to-End Robotic Reinforcement Learning without Reward Engineering

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

UVeQFed: Universal Vector Quantization for Federated Learning

A Geometric Analysis of Neural Collapse with Unconstrained Features

CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion

Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates

Efficient Exploration via State Marginal Matching

A Primer on the Signature Method in Machine Learning

Learning to Detect Malicious Clients for Robust Federated Learning

Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Capsules for Object Segmentation

Meta-Learning Probabilistic Inference For Prediction

Med-HALT: Medical Domain Hallucination Test for Large Language Models

Towards Sparse Hierarchical Graph Classifiers

Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours

Multi-graph Fusion for Multi-view Spectral Clustering

Label Efficient Learning of Transferable Representations across Domains and Tasks

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Discrete Variational Autoencoders

Why is Posterior Sampling Better than Optimism for Reinforcement Learning?

FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data

Near-Optimal Algorithms for Minimax Optimization

Statistical inference on random dot product graphs: a survey

Splitting Methods for Convex Clustering

Adversarial examples for generative models

On the Power of Over-parametrization in Neural Networks with Quadratic Activation

Self-Distillation Amplifies Regularization in Hilbert Space

Learning Stochastic Recurrent Networks