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.

Search paper metadata (including unsummarized papers)

7,741 to 7,800 of 9,704

Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization
Gustavo Sutter, Alejandro Comas-Leon, David Holzmüller +4
cs.LGcs.AIstat.MLarXiv:2610.08969v12026
Provably Faster Algorithms for Bilevel Optimization
Junjie Yang, Kaiyi Ji, Yingbin Liang
cs.LGmath.OCstat.MLarXiv:2106.04692v22021
Generalized Decision Transformer for Offline Hindsight Information Matching
Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu
cs.LGcs.AIstat.MLarXiv:2111.10364v32021
Simple, Efficient, and Neural Algorithms for Sparse Coding
Sanjeev Arora, Rong Ge, Tengyu Ma +1
cs.LGcs.DScs.NEarXiv:1503.00778v12015
Trust-Region Optimization for Smooth Potential-Interaction Energies in Wasserstein Space
You Wan, Ting Gao, Jinqiao Duan
stat.MLcs.LGmath.OCarXiv:2610.08883v12026
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild +2
cs.LGcs.AIstat.MLarXiv:2106.01342v12021
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
Koen Helwegen, James Widdicombe, Lukas Geiger +3
cs.LGstat.MLarXiv:1906.02107v22019
Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?
Raja Giryes, Guillermo Sapiro, Alex M. Bronstein
cs.NEcs.LGstat.MLarXiv:1504.08291v52015
Debiasing Evaluations That are Biased by Evaluations
Jingyan Wang, Ivan Stelmakh, Yuting Wei +1
stat.MLcs.ITcs.LGarXiv:2012.00714v12020
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
Santiago Silva, Boris Gutman, Eduardo Romero +3
stat.MLcs.LGq-bio.NCarXiv:1810.08553v42018
Machine Unlearning via Algorithmic Stability
Enayat Ullah, Tung Mai, Anup Rao +2
cs.LGstat.MLarXiv:2102.13179v12021
Statistical Benefits of Fine-Tuning from Pretrained Initialization in Diagonal Linear Networks
Alexandre Declèves, Etienne Boursier, Nicolas Flammarion
stat.MLcs.LGarXiv:2609.34756v12026
Learning Features of Music from Scratch
John Thickstun, Zaid Harchaoui, Sham Kakade
stat.MLcs.LGcs.SDarXiv:1611.09827v22016
Approximate Inference Turns Deep Networks into Gaussian Processes
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi +1
stat.MLcs.AIcs.LGarXiv:1906.01930v32019
Anomaly Detection using One-Class Neural Networks
Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla
cs.LGcs.NEstat.MLarXiv:1802.06360v22018
Taking Advantage of Sparsity in Multi-Task Learning
Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov +1
stat.MLmath.STarXiv:0903.1468v12009
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis +1
cs.LGcs.AIcs.CLarXiv:2602.15327v22026
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Youngseog Chung, Willie Neiswanger, Ian Char +1
cs.LGstat.MLarXiv:2011.09588v42020
Benign Overfitting under Heterogeneous Input Fusion
Houzhen Liu, Xiaobo Xia
cs.LGstat.MLarXiv:2610.09340v12026
The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression
Emmanuel J. Candes, Pragya Sur
stat.MEstat.MLarXiv:1804.09753v12018
Deep Learning-Based Communication Over the Air
Sebastian Dörner, Sebastian Cammerer, Jakob Hoydis +1
stat.MLcs.ITarXiv:1707.03384v12017
Policy Poisoning in Batch Reinforcement Learning and Control
Yuzhe Ma, Xuezhou Zhang, Wen Sun +1
cs.LGstat.MLarXiv:1910.05821v22019
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
Fanghui Liu, Xiaolin Huang, Yudong Chen +1
stat.MLcs.LGarXiv:2004.11154v52020
Two-Timescale Fine-tuning Provably Learns New Features for Two-Layer ReLU Networks
Etienne Boursier, Nicolas Flammarion
stat.MLcs.LGarXiv:2609.34667v12026
Quantum Machine Learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti +3
quant-phcond-mat.str-elstat.MLarXiv:1611.09347v22016
Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions
Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi +6
eess.AScs.LGcs.SDarXiv:2106.04492v22021
Quantile Regression Under Memory Constraint
Xi Chen, Weidong Liu, Yichen Zhang
stat.MEecon.EMstat.MLarXiv:1810.08264v12018
The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks
George Philipp, Jaime G. Carbonell
cs.LGcs.CVstat.MLarXiv:1806.00179v22018
Learning Representations for Counterfactual Inference
Fredrik D. Johansson, Uri Shalit, David Sontag
stat.MLcs.AIcs.LGarXiv:1605.03661v32016
Graph Kernels: State-of-the-Art and Future Challenges
Karsten Borgwardt, Elisabetta Ghisu, Felipe Llinares-López +2
cs.LGstat.MLarXiv:2011.03854v22020
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Xiao Ma, Liqin Zhao, Guan Huang +4
stat.MLcs.IRcs.LGarXiv:1804.07931v22018
A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing
Xiaoxing Ren, Thomas Parisini, Andreas A. Malikopoulos
math.OCcs.LGeess.SYarXiv:2609.26783v12026
De-identification of Patient Notes with Recurrent Neural Networks
Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner +1
cs.CLcs.AIcs.NEarXiv:1606.03475v12016
Summaries:한국어
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Gaël Varoquaux, Pradeep Reddy Raamana, Denis Engemann +3
stat.MLarXiv:1606.05201v22016
ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach
Kaichuang Yang, Håvard Rue, Jakob Zeitler
stat.MLcs.LGarXiv:2610.12213v12026
Sparse Matrix Inversion with Scaled Lasso
Tingni Sun, Cun-Hui Zhang
math.STstat.MLarXiv:1202.2723v22012
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings
Nicholas Rhinehart, Rowan McAllister, Kris Kitani +1
cs.CVcs.AIcs.LGarXiv:1905.01296v32019
Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
Chaoyue Liu, Libin Zhu, Mikhail Belkin
cs.LGmath.OCstat.MLarXiv:2003.00307v22020
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis
Rong Ge, Chi Jin, Yi Zheng
cs.LGmath.OCstat.MLarXiv:1704.00708v12017
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
Masatoshi Uehara, Wen Sun
cs.LGcs.AIstat.MLarXiv:2107.06226v42021
A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos +6
cs.LGstat.MLarXiv:2301.12594v22023
Symmetry-Aware Feature Learning: A Polynomial Separation for Multi-Index Models
Jivan Waber, Vanessa Piccolo, Yatin Dandi +1
cs.LGmath.PRstat.MLarXiv:2610.08420v12026
A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons
Yi-Cheng Chen, Ping-En Lu, Cheng-Shang Chang +1
q-bio.PEcs.LGstat.MLarXiv:2003.00122v62020
On Empirical Comparisons of Optimizers for Deep Learning
Dami Choi, Christopher J. Shallue, Zachary Nado +3
cs.LGstat.MLarXiv:1910.05446v32019
On the Number of Linear Regions of Deep Neural Networks
Guido Montúfar, Razvan Pascanu, Kyunghyun Cho +1
stat.MLcs.LGcs.NEarXiv:1402.1869v22014
Representation Tradeoffs for Hyperbolic Embeddings
Christopher De Sa, Albert Gu, Christopher Ré +1
cs.LGstat.MLarXiv:1804.03329v22018
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks
Xiaoli Chen, Liu Yang, Jinqiao Duan +1
physics.comp-phcs.LGstat.MLarXiv:2008.10653v12020
Controllable Pareto Multi-Task Learning
Xi Lin, Zhiyuan Yang, Qingfu Zhang +1
cs.LGstat.MLarXiv:2010.06313v22020
Building Normalizing Flows with Stochastic Interpolants
Michael S. Albergo, Eric Vanden-Eijnden
cs.LGstat.MLarXiv:2209.15571v32022
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
Sindy Löwe, David Madras, Richard Zemel +1
cs.LGstat.MLarXiv:2006.10833v32020
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
stat.MLcs.LGarXiv:1402.1389v22014
Signal Recovery on Graphs: Variation Minimization
Siheng Chen, Aliaksei Sandryhaila, José M. F. Moura +1
cs.SIcs.LGstat.MLarXiv:1411.7414v32014
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Qinghua Liu, Tiancheng Yu, Yu Bai +1
cs.LGcs.AIstat.MLarXiv:2010.01604v22020
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
Mauricio Sadinle, Jing Lei, Larry Wasserman
stat.MEcs.LGstat.MLarXiv:1609.00451v22016
Clustering Stability: An Overview
Ulrike von Luxburg
stat.MLarXiv:1007.1075v12010
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
Namrata Vaswani, Thierry Bouwmans, Sajid Javed +1
cs.ITcs.CVstat.MEarXiv:1711.09492v42017
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Garrett B. Goh, Charles Siegel, Abhinav Vishnu +2
stat.MLcs.AIcs.CEarXiv:1706.06689v12017
BootsTAP: Bootstrapped Training for Tracking-Any-Point
Carl Doersch, Pauline Luc, Yi Yang +8
cs.CVstat.MLarXiv:2402.00847v22024
Towards Best Practice in Explaining Neural Network Decisions with LRP
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima +3
cs.LGcs.CVstat.MLarXiv:1910.09840v32019
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik +4
cs.LGcs.AIcs.CLarXiv:2312.02119v32023

Machine Learning (stat)

Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization

Provably Faster Algorithms for Bilevel Optimization

Generalized Decision Transformer for Offline Hindsight Information Matching

Simple, Efficient, and Neural Algorithms for Sparse Coding

Trust-Region Optimization for Smooth Potential-Interaction Energies in Wasserstein Space

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization

Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?

Debiasing Evaluations That are Biased by Evaluations

Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

Machine Unlearning via Algorithmic Stability

Statistical Benefits of Fine-Tuning from Pretrained Initialization in Diagonal Linear Networks

Learning Features of Music from Scratch

Approximate Inference Turns Deep Networks into Gaussian Processes

Anomaly Detection using One-Class Neural Networks

Taking Advantage of Sparsity in Multi-Task Learning

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification

Benign Overfitting under Heterogeneous Input Fusion

The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression

Deep Learning-Based Communication Over the Air

Policy Poisoning in Batch Reinforcement Learning and Control

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond

Two-Timescale Fine-tuning Provably Learns New Features for Two-Layer ReLU Networks

Quantum Machine Learning

Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions

Quantile Regression Under Memory Constraint

The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks

Learning Representations for Counterfactual Inference

Graph Kernels: State-of-the-Art and Future Challenges

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing

De-identification of Patient Notes with Recurrent Neural Networks

Assessing and tuning brain decoders: cross-validation, caveats, and guidelines

ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach

Sparse Matrix Inversion with Scaled Lasso

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

Loss landscapes and optimization in over-parameterized non-linear systems and neural networks

No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis

Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage

A theory of continuous generative flow networks

Symmetry-Aware Feature Learning: A Polynomial Separation for Multi-Index Models

A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons

On Empirical Comparisons of Optimizers for Deep Learning

On the Number of Linear Regions of Deep Neural Networks

Representation Tradeoffs for Hyperbolic Embeddings

Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks

Controllable Pareto Multi-Task Learning

Building Normalizing Flows with Stochastic Interpolants

Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

Signal Recovery on Graphs: Variation Minimization

A Sharp Analysis of Model-based Reinforcement Learning with Self-Play

Least Ambiguous Set-Valued Classifiers with Bounded Error Levels

Clustering Stability: An Overview

Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

BootsTAP: Bootstrapped Training for Tracking-Any-Point

Towards Best Practice in Explaining Neural Network Decisions with LRP

Tree of Attacks: Jailbreaking Black-Box LLMs Automatically