JMLR

Dependent randomized rounding for clustering and partition systems with knapsack constraints

David G. Harris, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh

Comments In the Journal version of this paper, there is a small error in Proposition 25. This version of the paper fixes the error (see Proposition 5.4 in the arxiv version)

Journal ref Journal of Machine Learning Research 23(81), pp. 1-41 (2022)

Seeded graph matching for the correlated Gaussian Wigner model via the projected power method

Ernesto Araya, Guillaume Braun, Hemant Tyagi

Journal ref Journal of Machine Learning Research 25(5), 1-43, 2024

Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar

Journal ref Journal of Machine Learning Research (2023) Volume 24, Issue 1, Article No. 388, pp 18593-18618

Neural Operator: Learning Maps Between Function Spaces

Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar

Journal ref The Journal of Machine Learning Research (2023), Volume 24, Issue 1, Article No 89, pp 4061-4157

Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection

Xinyi Wang, Lang Tong

Journal ref The Journal of Machine Learning Research, Vol 23, Issue 1, pp. 2347-2373, 2022

Algorithms for Learning Kernels Based on Centered Alignment

Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh

Journal ref Journal of Machine Learning Research 13 (2012) 795-828

tsbootstrap: Enhancing Time Series Analysis with Advanced Bootstrapping Techniques

Sankalp Gilda, Benedikt Heidrich, Franz Kiraly

Comments 16 pages, 2 references, submitted to JMLR

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box

Ryan Giordano, Martin Ingram, Tamara Broderick

Journal ref Journal of Machine Learning Research, 2024, Volume 25, number 18, pages 1--39

A Discrete Variational Derivation of Accelerated Methods in Optimization

Cédric M. Campos, Alejandro Mahillo, David Martín de Diego

Comments 33 pages, 11 figures

Journal ref Journal of Machine Learning Research 24 (2023) 25:1-33

An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization

Guy Kornowski, Ohad Shamir

Comments Accepted to Journal of Machine Learning Research (JMLR); improved dependence on Lipschitz constant; some minor edits following reviews

Exploration of the search space of Gaussian graphical models for paired data

Alberto Roverato, Dung Ngoc Nguyen

Journal ref Journal of Machine Learning Research (2024)

Causal-learn: Causal Discovery in Python

Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang

Journal ref Journal of Machine Learning Research 25 (2024)

Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space

Zhengdao Chen

Comments 65 pages, 3 figures. Published by the Journal of Machine Learning Research and presented partially at the 40th International Conference on Machine Learning (ICML 2023)

Journal ref Journal of Machine Learning Research, 25(109):1-65, 2024

Pareto Smoothed Importance Sampling

Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry

Comments The final version with minor corrections. To be published in JMLR. 58 pages

Journal ref Journal of Machine Learning Research, 25(72):1-58, 2024

Mixed Regression via Approximate Message Passing

Nelvin Tan, Ramji Venkataramanan

Comments 44 pages. To appear in the Journal of Machine Learning Research. A shorter version of this paper appeared in the proceedings of AISTATS 2023

Journal ref Journal of Machine Learning Research, vol. 24, no. 317, pp. 1-44, 2023

Nearest Neighbor Dirichlet Mixtures

Shounak Chattopadhyay, Antik Chakraborty, David B. Dunson

Journal ref Journal of Machine Learning Research, 24(261), 1-46 (2023)

On the Learnability of Out-of-distribution Detection

Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu

Comments Accepted by JMLR in 7th of April, 2024. This is a journal extension of the previous NeurIPS 2022 Outstanding Paper "Is Out-of-distribution Detection Learnable?" [arXiv:2210.14707]

Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces

Jonathan W. Siegel

Journal ref Journal of Machine Learning Research 24.357 (2023): 1-52

Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization

Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou

Comments V3 changes: improve the presentation (highlighting the techniques); V2 changes: Updated to improve the presentation (emphasizing the importance of gradient-variation bounds and the contribution of Sword++ algorithm)

Journal ref Journal of Machine Learning Research, 2024

Resource-Efficient Neural Networks for Embedded Systems

Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani

Comments arXiv admin note: text overlap with arXiv:1812.02240; accepted at JMLR

abess: A Fast Best Subset Selection Library in Python and R

Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu

Journal ref Journal of Machine Learning Research (2022)

Adaptive learning of density ratios in RKHS

Werner Zellinger, Stefan Kindermann, Sergei V. Pereverzyev

Journal ref Journal of Machine Learning Research 24 (395), 1-28, 2023

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick

Comments Journal of Machine Learning Research (JMLR)

Policy Gradient Methods in the Presence of Symmetries and State Abstractions

Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup

Comments Published in the Journal of Machine Learning Research (JMLR). arXiv admin note: text overlap with arXiv:2209.07364

Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour

Rhys Howard, Lars Kunze

Comments 26 Pages = 13 Pages (Main Content) + 5 Pages (References) + 8 Pages (Appendix), 2 Figures, To be published in the Proceedings of the 2nd Conference on Causal Learning and Reasoning as part of the Journal of Machine Learning Research Workshop and Conference Proceedings series, Final submission version; Updated from initial to final submission version

Journal ref Proceedings of the Second Conference on Causal Learning and Reasoning, PMLR 213:473-498, 2023

Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization

Lorenzo Pacchiardi, Rilwan Adewoyin, Peter Dueben, Ritabrata Dutta

Journal ref Journal of Machine Learning Research, 25(45), 2024

Consistent Model-based Clustering: using the Quasi-Bernoulli Stick-Breaking Process

Cheng Zeng, Jeffrey W. Miller, Leo L. Duan

Comments 36 pages, 11 figures

Journal ref Journal of Machine Learning Research, 24(153):1--32, 2023

Scaling the Convex Barrier with Sparse Dual Algorithms

Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar

Comments Journal of Machine Learning Research, 2024 (extension of ICLR 2021 paper in [v1])

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models

Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh

Journal ref Journal of Machine Learning Research, 25 (2024), pp. 1-46

What to Do When Your Discrete Optimization Is the Size of a Neural Network?

Hugo Silva, Martha White

Comments Submitted to JMLR