Journal of Machine Learning Research最新文献

DisC²o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data. 用于分析现实世界高维数据的协变量移位的分布式因果推理。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2025-01-01

Jiayi Tong, Jie Hu, George Hripcsak, Yang Ning, Yong Chen

{"title":"DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data.","authors":"Jiayi Tong, Jie Hu, George Hripcsak, Yang Ning, Yong Chen","doi":"","DOIUrl":"","url":null,"abstract":"High-dimensional healthcare data, such as electronic health records (EHR) data and claims data, present two primary challenges due to the large number of variables and the need to consolidate data from multiple clinical sites. The third key challenge is the potential existence of heterogeneity in terms of covariate shift. In this paper, we propose a distributed learning algorithm accounting for covariate shift to estimate the average treatment effect (ATE) for high-dimensional data, named DisC2o-HD. Leveraging the surrogate likelihood method, our method calibrates the estimates of the propensity score and outcome models to approximately attain the desired covariate balancing property, while accounting for the covariate shift across multiple clinical sites. We show that our distributed covariate balancing propensity score estimator can approximate the pooled estimator, which is obtained by pooling the data from multiple sites together. The proposed estimator remains consistent if either the propensity score model or the outcome regression model is correctly specified. The semiparametric efficiency bound is achieved when both the propensity score and the outcome models are correctly specified. We conduct simulation studies to demonstrate the performance of the proposed algorithm; additionally, we apply the algorithm to a real-world data set to present the readiness of implementation and validity.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"26 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12269483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660933","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

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

Spatial meshing for general Bayesian multivariate models. 一般贝叶斯多元模型的空间网格划分。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2024-03-01

Michele Peruzzi, David B Dunson

{"title":"Spatial meshing for general Bayesian multivariate models.","authors":"Michele Peruzzi, David B Dunson","doi":"","DOIUrl":"","url":null,"abstract":"Quantifying spatial and/or temporal associations in multivariate geolocated data of different types is achievable via spatial random effects in a Bayesian hierarchical model, but severe computational bottlenecks arise when spatial dependence is encoded as a latent Gaussian process (GP) in the increasingly common large scale data settings on which we focus. The scenario worsens in non-Gaussian models because the reduced analytical tractability leads to additional hurdles to computational efficiency. In this article, we introduce Bayesian models of spatially referenced data in which the likelihood or the latent process (or both) are not Gaussian. First, we exploit the advantages of spatial processes built via directed acyclic graphs, in which case the spatial nodes enter the Bayesian hierarchy and lead to posterior sampling via routine Markov chain Monte Carlo (MCMC) methods. Second, motivated by the possible inefficiencies of popular gradient-based sampling approaches in the multivariate contexts on which we focus, we introduce the simplified manifold preconditioner adaptation (SiMPA) algorithm which uses second order information about the target but avoids expensive matrix operations. We demostrate the performance and efficiency improvements of our methods relative to alternatives in extensive synthetic and real world remote sensing and community ecology applications with large scale data at up to hundreds of thousands of spatial locations and up to tens of outcomes. Software for the proposed methods is part of R package meshed, available on CRAN.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"25 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592821","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

Causal Discovery with Generalized Linear Models through Peeling Algorithms. 基于剥离算法的广义线性模型因果发现。

IF 4.3 3区计算机科学

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

Minjie Wang, Xiaotong Shen, Wei Pan

{"title":"Causal Discovery with Generalized Linear Models through Peeling Algorithms.","authors":"Minjie Wang, Xiaotong Shen, Wei Pan","doi":"","DOIUrl":"","url":null,"abstract":"This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships. The proposed approach addresses this issue by developing two peeling algorithms (bottom-up and top-down) to ascertain causal relationships and valid instruments. This approach first reconstructs a super-graph to represent ancestral relationships between variables, using a peeling algorithm based on nodewise GLM regressions that exploit relationships between primary and instrumental variables. Then, it estimates parent-child effects from the ancestral relationships using another peeling algorithm while deconfounding a child's model with information borrowed from its parents' models. The article offers a theoretical analysis of the proposed approach, establishing conditions for model identifiability and providing statistical guarantees for accurately discovering parent-child relationships via the peeling algorithms. Furthermore, the article presents numerical experiments showcasing the effectiveness of our approach in comparison to state-of-the-art structure learning methods without confounders. Lastly, it demonstrates an application to Alzheimer's disease (AD), highlighting the method's utility in constructing gene-to-gene and gene-to-disease regulatory networks involving Single Nucleotide Polymorphisms (SNPs) for healthy and AD subjects.","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/PMC11699566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933418","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

Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning. 重新思考折扣正则化：强化学习中正则化的新解释、意外后果和解决方案。

IF 4.3 3区计算机科学

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

Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A Murphy, Finale Doshi-Velez

{"title":"Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning.","authors":"Sarah Rathnam, Sonali Parbhoo, Siddharth Swaroop, Weiwei Pan, Susan A Murphy, Finale Doshi-Velez","doi":"","DOIUrl":"","url":null,"abstract":"Discount regularization, using a shorter planning horizon when calculating the optimal policy, is a popular choice to avoid overfitting when faced with sparse or noisy data. It is commonly interpreted as de-emphasizing or ignoring delayed effects. In this paper, we prove two alternative views of discount regularization that expose unintended consequences and motivate novel regularization methods. In model-based RL, planning under a lower discount factor acts like a prior with stronger regularization on state-action pairs with more transition data. This leads to poor performance when the transition matrix is estimated from data sets with uneven amounts of data across state-action pairs. In model-free RL, discount regularization equates to planning using a weighted average Bellman update, where the agent plans as if the values of all state-action pairs are closer than implied by the data. Our equivalence theorems motivate simple methods that generalize discount regularization by setting parameters locally for individual state-action pairs rather than globally. We demonstrate the failures of discount regularization and how we remedy them using our state-action-specific methods across empirical examples with both tabular and continuous state spaces.","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/PMC12058221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056986","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