Journal of Machine Learning Research最新文献

Flexible Bayesian Product Mixture Models for Vector Autoregressions. 灵活的贝叶斯向量自回归产品混合物模型

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2024-04-01

Suprateek Kundu, Joshua Lukemire

{"title":"Flexible Bayesian Product Mixture Models for Vector Autoregressions.","authors":"Suprateek Kundu, Joshua Lukemire","doi":"","DOIUrl":"","url":null,"abstract":"Bayesian non-parametric methods based on Dirichlet process mixtures have seen tremendous success in various domains and are appealing in being able to borrow information by clustering samples that share identical parameters. However, such methods can face hurdles in heterogeneous settings where objects are expected to cluster only along a subset of axes or where clusters of samples share only a subset of identical parameters. We overcome such limitations by developing a novel class of product of Dirichlet process location-scale mixtures that enables independent clustering at multiple scales, which results in varying levels of information sharing across samples. First, we develop the approach for independent multivariate data. Subsequently we generalize it to multivariate time-series data under the framework of multi-subject Vector Autoregressive (VAR) models that is our primary focus, which go beyond parametric single-subject VAR models. We establish posterior consistency and develop efficient posterior computation for implementation. Extensive numerical studies involving VAR models show distinct advantages over competing methods in terms of estimation, clustering, and feature selection accuracy. Our resting state fMRI analysis from the Human Connectome Project reveals biologically interpretable connectivity differences between distinct intelligence groups, while another air pollution application illustrates the superior forecasting accuracy compared to alternate methods.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"25 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11646655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect-Invariant Mechanisms for Policy Generalization. 政策通用化的效应不变机制。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2024-01-01

Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters

{"title":"Effect-Invariant Mechanisms for Policy Generalization.","authors":"Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters","doi":"","DOIUrl":"","url":null,"abstract":"Policy learning is an important component of many real-world learning systems. A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks. Recently, it has been suggested to exploit invariant conditional distributions to learn models that generalize better to unseen environments. However, assuming invariance of entire conditional distributions (which we call full invariance) may be too strong of an assumption in practice. In this paper, we introduce a relaxation of full invariance called effect-invariance (e-invariance for short) and prove that it is sufficient, under suitable assumptions, for zero-shot policy generalization. We also discuss an extension that exploits e-invariance when we have a small sample from the test environment, enabling few-shot policy generalization. Our work does not assume an underlying causal graph or that the data are generated by a structural causal model; instead, we develop testing procedures to test e-invariance directly from data. We present empirical results using simulated data and a mobile health intervention dataset to demonstrate the effectiveness of our approach.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"25 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11286230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonparametric Regression for 3D Point Cloud Learning. 用于 3D 点云学习的非参数回归。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2024-01-01

Xinyi Li, Shan Yu, Yueying Wang, Guannan Wang, Li Wang, Ming-Jun Lai

{"title":"Nonparametric Regression for 3D Point Cloud Learning.","authors":"Xinyi Li, Shan Yu, Yueying Wang, Guannan Wang, Li Wang, Ming-Jun Lai","doi":"","DOIUrl":"","url":null,"abstract":"In recent years, there has been an exponentially increased amount of point clouds collected with irregular shapes in various areas. Motivated by the importance of solid modeling for point clouds, we develop a novel and efficient smoothing tool based on multivariate splines over the triangulation to extract the underlying signal and build up a 3D solid model from the point cloud. The proposed method can denoise or deblur the point cloud effectively, provide a multi-resolution reconstruction of the actual signal, and handle sparse and irregularly distributed point clouds to recover the underlying trajectory. In addition, our method provides a natural way of numerosity data reduction. We establish the theoretical guarantees of the proposed method, including the convergence rate and asymptotic normality of the estimator, and show that the convergence rate achieves optimal nonparametric convergence. We also introduce a bootstrap method to quantify the uncertainty of the estimators. Through extensive simulation studies and a real data example, we demonstrate the superiority of the proposed method over traditional smoothing methods in terms of estimation accuracy and efficiency of data reduction.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"25 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11465206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142401809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data. 用于对不可交换分组数据进行聚类的图形 Dirichlet Process。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2024-01-01

Arhit Chakrabarti, Yang Ni, Ellen Ruth A Morris, Michael L Salinas, Robert S Chapkin, Bani K Mallick

引用次数: 0

Convergence for nonconvex ADMM, with applications to CT imaging. 非凸 ADMM 的收敛性，并应用于 CT 成像。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2024-01-01

Rina Foygel Barber, Emil Y Sidky

{"title":"Convergence for nonconvex ADMM, with applications to CT imaging.","authors":"Rina Foygel Barber, Emil Y Sidky","doi":"","DOIUrl":"","url":null,"abstract":"The alternating direction method of multipliers (ADMM) algorithm is a powerful and flexible tool for complex optimization problems of the form <math><mi>m</mi> <mi>i</mi> <mi>n</mi> <mo>{</mo> <mi>f</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> <mo>+</mo> <mi>g</mi> <mo>(</mo> <mi>y</mi> <mo>)</mo> <mspace></mspace> <mo>:</mo> <mspace></mspace> <mi>A</mi> <mi>x</mi> <mo>+</mo> <mi>B</mi> <mi>y</mi> <mo>=</mo> <mi>c</mi> <mo>}</mo></math> . ADMM exhibits robust empirical performance across a range of challenging settings including nonsmoothness and nonconvexity of the objective functions <math><mi>f</mi></math> and <math><mi>g</mi></math> , and provides a simple and natural approach to the inverse problem of image reconstruction for computed tomography (CT) imaging. From the theoretical point of view, existing results for convergence in the nonconvex setting generally assume smoothness in at least one of the component functions in the objective. In this work, our new theoretical results provide convergence guarantees under a restricted strong convexity assumption without requiring smoothness or differentiability, while still allowing differentiable terms to be treated approximately if needed. We validate these theoretical results empirically, with a simulated example where both <math><mi>f</mi></math> and <math><mi>g</mi></math> are nondifferentiable-and thus outside the scope of existing theory-as well as a simulated CT image reconstruction problem.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"25 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11155492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Batch Normalization Preconditioning for Stochastic Gradient Langevin Dynamics 随机梯度朗格万动力学的批归一化预处理

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.220726a

Susanne Lange, Wei Deng, Q. Ye, Guang Lin

引用次数: 2

A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization with NonIsolated Local Minima 具有非孤立局部极小值的非凸优化随机梯度下降法的局部收敛理论

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.230106

Taehee Ko and Xiantao Li

引用次数: 0

Efficient Anti-Symmetrization of a Neural Network Layer by Taming the Sign Problem 基于驯服符号问题的神经网络层的有效抗对称

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.230703

Nilin Abrahamsen and Lin Lin

引用次数: 0

A Brief Survey on the Approximation Theory for Sequence Modelling 序列建模的近似理论综述

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.221221

Haotian Jiang, Qianxiao Li, Zhong Li null, Shida Wang

引用次数: 0

Reinforcement Learning with Function Approximation: From Linear to Nonlinear 函数逼近的强化学习:从线性到非线性

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.230105

Jihao Long and Jiequn Han

引用次数: 0