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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3,601 to 3,660 of 6,771

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
Jiahao Chen, Nathan Kallus, Xiaojie Mao +2
stat.APstat.MLarXiv:1811.11154v12018
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
physics.comp-phcs.LGhep-exarXiv:1807.02876v32018
The Consciousness Prior
Yoshua Bengio
cs.LGcs.AIstat.MLarXiv:1709.08568v22017
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
cs.LGstat.MLarXiv:1907.04275v32019
Variational Recurrent Auto-Encoders
Otto Fabius, Joost R. van Amersfoort
stat.MLcs.LGcs.NEarXiv:1412.6581v62014
Towards a mathematical theory of superposition
Michael I. Ivanitskiy, John Jasper, Emily J. King +1
stat.MLcs.ITcs.LGarXiv:2608.27540v12026
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan
cs.LGstat.MLarXiv:2002.02561v72020
Deep Machine Learning Approach to Develop a New Asphalt Pavement Condition Index
Hamed Majidifard, Yaw Adu-Gyamfi, William G. Buttlar
stat.MLcs.LGstat.COarXiv:2004.13314v12020
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
Belinda Tzen, Maxim Raginsky
cs.LGstat.MLarXiv:1905.09883v22019
Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network
Anmol Sharma, Ghassan Hamarneh
eess.IVcs.AIcs.CVarXiv:1904.12200v32019
Respecting causality is all you need for training physics-informed neural networks
Sifan Wang, Shyam Sankaran, Paris Perdikaris
cs.LGmath.NAnlin.CDarXiv:2203.07404v12022
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
Wengong Jin, Kevin Yang, Regina Barzilay +1
cs.LGcs.AIcs.NEarXiv:1812.01070v32018
Flow Matching on General Geometries
Ricky T. Q. Chen, Yaron Lipman
cs.LGcs.AIstat.MLarXiv:2302.03660v32023
Stochastic blockmodels with growing number of classes
David S. Choi, Patrick J. Wolfe, Edoardo M. Airoldi
math.STcs.SIstat.MEarXiv:1011.4644v22010
Neural Programmer: Inducing Latent Programs with Gradient Descent
Arvind Neelakantan, Quoc V. Le, Ilya Sutskever
cs.LGcs.CLstat.MLarXiv:1511.04834v32015
Shift-Invariance Sparse Coding for Audio Classification
Roger Grosse, Rajat Raina, Helen Kwong +1
cs.LGstat.MLarXiv:1206.5241v12012
Interpreting Tree Ensembles with inTrees
Houtao Deng
cs.LGstat.MLarXiv:1408.5456v12014
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade +2
cs.LGcs.AIcs.ROarXiv:1811.01848v32018
Infinite Mixture Prototypes for Few-Shot Learning
Kelsey R. Allen, Evan Shelhamer, Hanul Shin +1
cs.LGstat.MLarXiv:1902.04552v12019
Unmasking DeepFakes with simple Features
Ricard Durall, Margret Keuper, Franz-Josef Pfreundt +1
cs.LGcs.CVstat.MLarXiv:1911.00686v32019
Practical Gauss-Newton Optimisation for Deep Learning
Aleksandar Botev, Hippolyt Ritter, David Barber
stat.MLarXiv:1706.03662v22017
Will Artificial Intelligence supersede Earth System and Climate Models?
Christopher Irrgang, Niklas Boers, Maike Sonnewald +4
stat.MLcs.LGphysics.ao-pharXiv:2101.09126v12021
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei, Kendrick Shen, Yining Chen +1
cs.LGstat.MLarXiv:2010.03622v52020
Learning from Between-class Examples for Deep Sound Recognition
Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada
cs.LGcs.SDeess.ASarXiv:1711.10282v22017
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy +10
cs.LGstat.MLarXiv:2007.05566v12020
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
Shion Honda, Shoi Shi, Hiroki R. Ueda
cs.LGstat.MLarXiv:1911.04738v12019
One-Shot Federated Learning
Neel Guha, Ameet Talwalkar, Virginia Smith
cs.LGstat.MLarXiv:1902.11175v22019
Optimal transport mapping via input convex neural networks
Ashok Vardhan Makkuva, Amirhossein Taghvaei, Sewoong Oh +1
cs.LGstat.MLarXiv:1908.10962v22019
Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning
Zhanghan Ke, Daoye Wang, Qiong Yan +2
cs.LGcs.CVstat.MLarXiv:1909.01804v12019
Statistical Learning Theory: Models, Concepts, and Results
Ulrike von Luxburg, Bernhard Schoelkopf
stat.MLmath.STarXiv:0810.4752v12008
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning
Jinhyun So, Chaoyang He, Chien-Sheng Yang +5
cs.LGcs.CRcs.DCarXiv:2109.14236v32021
Adversarial vulnerability for any classifier
Alhussein Fawzi, Hamza Fawzi, Omar Fawzi
cs.LGcs.CRcs.CVarXiv:1802.08686v22018
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck
eess.SPcs.AIcs.LGarXiv:1909.10461v22019
Visual Object Networks: Image Generation with Disentangled 3D Representation
Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang +4
cs.CVcs.GRstat.MLarXiv:1812.02725v12018
Conformal Inference of Counterfactuals and Individual Treatment Effects
Lihua Lei, Emmanuel J. Candès
stat.MEmath.STstat.MLarXiv:2006.06138v22020
Bayesian Dark Knowledge
Anoop Korattikara, Vivek Rathod, Kevin Murphy +1
cs.LGstat.MLarXiv:1506.04416v32015
Online Convex Optimization with Stochastic Constraints
Hao Yu, Michael J. Neely, Xiaohan Wei
math.OCstat.MLarXiv:1708.03741v12017
Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli
stat.MLcs.LGmath.PRarXiv:2208.05314v22022
Deep ReLU Networks Have Surprisingly Few Activation Patterns
Boris Hanin, David Rolnick
stat.MLcs.LGmath.STarXiv:1906.00904v22019
Classification Accuracy Score for Conditional Generative Models
Suman Ravuri, Oriol Vinyals
cs.LGstat.MLarXiv:1905.10887v22019
Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
Chong You, Chun-Guang Li, Daniel P. Robinson +1
cs.LGcs.CVstat.MLarXiv:1605.02633v12016
On the Limitations of Unsupervised Bilingual Dictionary Induction
Anders Søgaard, Sebastian Ruder, Ivan Vulić
cs.CLcs.LGstat.MLarXiv:1805.03620v12018
Decentralized Deep Learning with Arbitrary Communication Compression
Anastasia Koloskova, Tao Lin, Sebastian U. Stich +1
cs.LGcs.DCcs.DSarXiv:1907.09356v32019
Curriculum Learning of Multiple Tasks
Anastasia Pentina, Viktoriia Sharmanska, Christoph H. Lampert
stat.MLcs.LGarXiv:1412.1353v12014
Learning a Size-Weight Frontier for Synthetic-Augmented Inference
Chengpiao Huang, Kaizheng Wang
stat.MEcs.AIcs.LGarXiv:2608.28576v12026
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Fuli Feng, Xiangnan He, Jie Tang +1
cs.LGcs.SIstat.MLarXiv:1902.08226v22019
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin +4
cs.LGcs.NEstat.MLarXiv:1912.08881v32019
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider +1
stat.MLarXiv:1703.06240v12017
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Lingxiao Wang, Qi Cai, Zhuoran Yang +1
cs.LGmath.OCstat.MLarXiv:1909.01150v32019
A Variational Perspective on Solving Inverse Problems with Diffusion Models
Morteza Mardani, Jiaming Song, Jan Kautz +1
cs.LGcs.CVmath.NAarXiv:2305.04391v22023
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz +2
cs.LGcs.AIstat.MLarXiv:2104.05522v62021
Defending against Backdoors in Federated Learning with Robust Learning Rate
Mustafa Safa Ozdayi, Murat Kantarcioglu, Yulia R. Gel
cs.LGcs.CRstat.MLarXiv:2007.03767v42020
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
Jason Jo, Yoshua Bengio
cs.LGstat.MLarXiv:1711.11561v12017
Random Feature Maps for Dot Product Kernels
Purushottam Kar, Harish Karnick
cs.LGcs.CGmath.FAarXiv:1201.6530v32012
Implicit Reparameterization Gradients
Michael Figurnov, Shakir Mohamed, Andriy Mnih
cs.LGstat.MLarXiv:1805.08498v42018
Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy, Yair Carmon, John C. Duchi +1
math.OCcs.LGstat.MLarXiv:2010.05893v22020
Learning Likelihoods with Conditional Normalizing Flows
Christina Winkler, Daniel Worrall, Emiel Hoogeboom +1
cs.LGcs.CVstat.MLarXiv:1912.00042v22019
Modeling Missing Data in Clinical Time Series with RNNs
Zachary C. Lipton, David C. Kale, Randall Wetzel
cs.LGcs.IRcs.NEarXiv:1606.04130v52016
Understanding Alternating Minimization for Matrix Completion
Moritz Hardt
cs.LGcs.DSstat.MLarXiv:1312.0925v42013
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
Jiangtao Xie, Fei Long, Jiaming Lv +2
cs.CVcs.LGstat.MLarXiv:2204.04567v12022

Machine Learning (stat)

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved

Machine Learning in High Energy Physics Community White Paper

The Consciousness Prior

Learning to Optimize Domain Specific Normalization for Domain Generalization

Variational Recurrent Auto-Encoders

Towards a mathematical theory of superposition

Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks

Deep Machine Learning Approach to Develop a New Asphalt Pavement Condition Index

Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network

Respecting causality is all you need for training physics-informed neural networks

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization

Flow Matching on General Geometries

Stochastic blockmodels with growing number of classes

Neural Programmer: Inducing Latent Programs with Gradient Descent

Shift-Invariance Sparse Coding for Audio Classification

Interpreting Tree Ensembles with inTrees

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

Infinite Mixture Prototypes for Few-Shot Learning

Unmasking DeepFakes with simple Features

Practical Gauss-Newton Optimisation for Deep Learning

Will Artificial Intelligence supersede Earth System and Climate Models?

Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Learning from Between-class Examples for Deep Sound Recognition

Contrastive Training for Improved Out-of-Distribution Detection

SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery

One-Shot Federated Learning

Optimal transport mapping via input convex neural networks

Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning

Statistical Learning Theory: Models, Concepts, and Results

LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning

Adversarial vulnerability for any classifier

Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

Visual Object Networks: Image Generation with Disentangled 3D Representation

Conformal Inference of Counterfactuals and Individual Treatment Effects

Bayesian Dark Knowledge

Online Convex Optimization with Stochastic Constraints

Convergence of denoising diffusion models under the manifold hypothesis

Deep ReLU Networks Have Surprisingly Few Activation Patterns

Classification Accuracy Score for Conditional Generative Models

Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering

On the Limitations of Unsupervised Bilingual Dictionary Induction

Decentralized Deep Learning with Arbitrary Communication Compression

Curriculum Learning of Multiple Tasks

Learning a Size-Weight Frontier for Synthetic-Augmented Inference

Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning

Multi-fidelity Bayesian Optimisation with Continuous Approximations

Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

A Variational Perspective on Solving Inverse Problems with Diffusion Models

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

Defending against Backdoors in Federated Learning with Robust Learning Rate

Measuring the tendency of CNNs to Learn Surface Statistical Regularities

Random Feature Maps for Dot Product Kernels

Implicit Reparameterization Gradients

Large-Scale Methods for Distributionally Robust Optimization

Learning Likelihoods with Conditional Normalizing Flows

Modeling Missing Data in Clinical Time Series with RNNs

Understanding Alternating Minimization for Matrix Completion

Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification