JMLR 2024

422 papers

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

BigScience Workshop

High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method

Yassine Laguel, Necdet Serhat Aybat, Mert Gurbuzbalaban

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang, Alex Hernández-García, Paula Harder, Venkatesh Ramesh, Prasanna Sattigeri

Mean-Field Games With Finitely Many Players: Independent Learning and Subjectivity

Bora Yongacoglu, Gürdal Arslan, Serdar Yüksel

Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique

Elissa Mhanna, Mohamad Assaad

PyDMD: A Python Package for Robust Dynamic Mode Decomposition

Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo

Stochastic-Constrained Stochastic Optimization with Markovian Data

Yeongjong Kim, Dabeen Lee

Estimation of the Order of Non-Parametric Hidden Markov Models using the Singular Values of an Integral Operator

Marie Du Roy de Chaumaray, Salima El Kolei, Marie-Pierre Etienne, Matthieu Marbac

Infinite-Dimensional Diffusion Models

Jakiw Pidstrigach, Youssef Marzouk, Sebastian Reich, Sven Wang

Multi-Response Linear Discriminant Analysis in High Dimensions

Kai Deng, Xin Zhang, Aaron J. Molstad

Towards Unbiased Exploration in Partial Label Learning

Zsolt Zombori, Agapi Rissaki, Kristóf Szabó, Wolfgang Gatterbauer, Michael Benedikt

Logistic Regression Under Network Dependence

Somabha Mukherjee, Ziang Niu, Sagnik Halder, Bhaswar B. Bhattacharya, George Michailidis

Nearest Neighbor Sampling for Covariate Shift Adaptation

François Portier, Lionel Truquet, Ikko Yamane

Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets

Benjamin Dupuis, Paul Viallard, George Deligiannidis, Umut Simsekli

Stage-Aware Learning for Dynamic Treatments

Hanwen Ye, Wenzhuo Zhou, Ruoqing Zhu, Annie Qu

Information-Theoretic Generalization Bounds for Transductive Learning and its Applications

Huayi Tang, Yong Liu

Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs

Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay, Samuel Vaiter

Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap

Indrila Ganguly, Srijan Sengupta, Sujit Ghosh

Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions

Natesh S. Pillai

Approximate Information Tests on Statistical Submanifolds

Michael W. Trosset, Carey E. Priebe

PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Sifan Wang, Bowen Li, Yuhan Chen, Paris Perdikaris

Deep Backward and Galerkin Methods for the Finite State Master Equation

Asaf Cohen, Mathieu Lauriere, Ethan Zell

A New, Physics-Informed Continuous-Time Reinforcement Learning Algorithm with Performance Guarantees

Brent A. Wallace, Jennie Si

Learning with a linear loss function: excess risk and estimation bounds for ERM, minmax MOM and their regularized versions with applications to robustness in sparse PCA.

Guillaume Lecue, Lucie Neirac

Robust Spectral Clustering with Rank Statistics

Joshua Cape, Xianshi Yu, Jonquil Z. Liao

Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach

Shuang Qiu, Boxiang Lyu, Qinglin Meng, Zhaoran Wang, Zhuoran Yang

Transfer learning for tensor Gaussian graphical models

Mingyang Ren, Yaoming Zhen, Junhui Wang

Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks

Naoki Sato, Koshiro Izumi, Hideaki Iiduka

Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression

Aleksandrs Slivkins, Xingyu Zhou, Karthik Abinav Sankararaman, Dylan J. Foster

An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification

Nicolás García Trillos, Matt Jacobs, Jakwang Kim, Matthew Werenski

Fisher information dissipation for time-inhomogeneous stochastic differential equations

Qi Feng, Xinzhe Zuo, Wuchen Li

Zeroth-order Stochastic Approximation Algorithms for DR-submodular Optimization

Yuefang Lian, Xiao Wang, Dachuan Xu, Zhongrui Zhao

Neural Bayes estimators for censored inference with peaks-over-threshold models

Jordan Richards, Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Raphaël Huser

Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints

Pierre Ablin, Simon Vary, Bin Gao, Pierre-antoine Absil

Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition

Varun Kanade, Patrick Rebeschini, Tomas Vaskevicius

Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization

Lesi Chen, Luo Luo

Learning Discretized Neural Networks under Ricci Flow

Jun Chen, Hanwen Chen, Mengmeng Wang, Guang Dai, Ivor W. Tsang

Deep Nonparametric Quantile Regression under Covariate Shift

Xingdong Feng, XIN HE, Yuling Jiao, Lican Kang, Caixing Wang

Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes

Chengkuan Hong, Christian R. Shelton, Jun Zhu

Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery

Zhen Qin, Michael B. Wakin, Zhihui Zhu

Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes

Su Jia, Fatemeh Navidi, viswanath nagarajan, R. Ravi

Decomposing Global Feature Effects Based on Feature Interactions

Julia Herbinger, Marvin N. Wright, Thomas Nagler, Bernd Bischl, Giuseppe Casalicchio

A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness

Yuze Han, Xiang Li, Shiyun Lin, Zhihua Zhang

Accelerating Nuclear-norm Regularized Low-rank Matrix Optimization Through Burer-Monteiro Decomposition

Ching-pei Lee, Ling Liang, Tianyun Tang, Kim-Chuan Toh

Targeted Separation and Convergence with Kernel Discrepancies

Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, Lester Mackey

Learning Gaussian DAGs from Network Data

Hangjian Li, OSCAR HERNAN MADRID PADILLA, Qing Zhou

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"

Daniel Paulin, Peter A. Whalley

KerasCV and KerasNLP: Multi-framework Models

Matthew Watson, Divyashree Shivakumar Sreepathihalli, Francois Chollet, Martin Górner, Kiranbir Sodhia

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains

Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea

An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems

Yuqia Wu, Shaohua Pan, Xiaoqi Yang

Learning Regularized Graphon Mean-Field Games with Unknown Graphons

Fengzhuo Zhang, Vincent Y. F. Tan, Zhaoran Wang, Zhuoran Yang

PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX

Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Shrinu Kushagra

Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm

Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov

A minimax optimal approach to high-dimensional double sparse linear regression

YanHang Zhang, zhifan Li, Shixiang Liu, Jianxin Yin

Localisation of Regularised and Multiview Support Vector Machine Learning

Aurelian Gheondea, Cankat Tilki

Consistent Multiclass Algorithms for Complex Metrics and Constraints

Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli

Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC

Marko Järvenpää, Jukka Corander

Topological Analysis for Detecting Anomalies in dependent sequences: application to Time Series

Frederic Chazal, Clément Levrard, Martin Royer

Operator learning without the adjoint

Nicolas Boulle, Diana Halikias, Samuel E. Otto, Alex Townsend

Entropic Gromov-Wasserstein Distances: Stability and Algorithms

Gabriel Rioux, Ziv Goldfeld, Kengo Kato

Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning

Jaesun Shin, Eunjoo Jeon, Taewon Cho, Namkyeong Cho, Youngjune Gwon

Gradual Domain Adaptation: Theory and Algorithms

Yifei He, Haoxiang Wang, Bo Li, Han Zhao

The Nyström method for convex loss functions

Andrea Della Vecchia, Ernesto De Vito, Jaouad Mourtada, Lorenzo Rosasco

Distributed Kernel-Driven Data Clustering

Ioannis Schizas

Uncertainty Quantification of MLE for Entity Ranking with Covariates

Jianqing Fan, Jikai Hou, Mengxin Yu

Federated Automatic Differentiation

Keith Rush, Zachary Charles, Zachary Garrett

On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training

Chen Liu, Zhichao Huang, Mathieu Salzmann, Tong Zhang, Sabine Süsstrunk

Fréchet Random Forests for Metric Space Valued Regression with Non Euclidean Predictors

Louis Capitaine, Jérémie Bigot, Rodolphe Thiébaut, Robin Genuer

Aequitas Flow: Streamlining Fair ML Experimentation

Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva, Beatriz M. Jorge, Rita P. Ribeiro

Information Capacity Regret Bounds for Bandits with Mediator Feedback

Khaled Eldowa, Nicolò Cesa-bianchi, Alberto Maria Metelli, Marcello Restelli

DAG-Informed Structure Learning from Multi-Dimensional Point Processes

Chunming Zhang, Muhong Gao, Shengji Jia

Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance

Jordan Awan, Aishwarya Ramasethu

A Rainbow in Deep Network Black Boxes

Florentin Guth, Brice Ménard, Gaspar Rochette, Stephane Mallat

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps

Simon Apers, Sander Gribling, Dániel Szilágyi

Memorization With Neural Nets: Going Beyond the Worst Case

Sjoerd Dirksen, Patrick Finke, Martin Genzel

PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates

Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

Causal effects of intervening variables in settings with unmeasured confounding

Lan Wen, Aaron Sarvet, Mats Stensrud

Lower Complexity Adaptation for Empirical Entropic Optimal Transport

Michel Groppe, Shayan Hundrieser

A Note on Entrywise Consistency for Mixed-data Matrix Completion

Yunxiao Chen, Xiaoou Li

A Characterization of Multioutput Learnability

Vinod Raman, UNIQUE SUBEDI, Ambuj Tewari

Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning

Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

Lower Bounds on the Bayesian Risk via Information Measures

Amedeo Roberto Esposito, Adrien Vandenbroucque, Michael Gastpar

Bayesian Structural Learning with Parametric Marginals for Count Data: An Application to Microbiota Systems

Veronica Vinciotti, Pariya Behrouzi, Reza Mohammadi

Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)

Jimmy Hickey, Jonathan P. Williams, Emily C. Hector

Inference on High-dimensional Single-index Models with Streaming Data

Dongxiao Han, Jinhan Xie, Jin Liu, Liuquan Sun, Jian Huang

On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport

Minhui Huang, Shiqian Ma, Lifeng LAI

ENNS: Variable Selection, Regression, Classification, and Deep Neural Network for High-Dimensional Data

Kaixu Yang, Arkaprabha Ganguli, Tapabrata Maiti

On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives

Tong Li, Ming Yuan

Open-Source Conversational AI with SpeechBrain 1.0

Mirco Ravanelli, Titouan Parcollet, Adel Moumen, Sylvain de Langen, Cem Subakan

Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components

Naichen Shi, Salar Fattahi, Raed AL Kontar

Generalization on the Unseen, Logic Reasoning and Degree Curriculum

Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk

Goal-Space Planning with Subgoal Models

Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White

Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Constrained Optimization

Enming Liang, Minghua Chen, Steven H. Low

Label Noise Robustness of Conformal Prediction

Bat-Sheva Einbinder, Shai Feldman, Stephen Bates, Anastasios N. Angelopoulos, Asaf Gendler

PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium

Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang

Geometric Learning with Positively Decomposable Kernels

Nathael Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega, Salem SAID

Mentored Learning: Improving Generalization and Convergence of Student Learner

Xiaofeng Cao, Yaming Guo, Heng Tao Shen, Ivor W. Tsang, James T. Kwok

Robust Principal Component Analysis using Density Power Divergence

Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh

Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data

Arhit Chakrabarti, Yang Ni, Ellen Ruth A. Morris, Michael L. Salinas, Robert S. Chapkin