Journal of Machine Learning Research最新文献

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":null,"pages":null},"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

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

IF 6 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":null,"pages":null},"PeriodicalIF":6.0,"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

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

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

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

Why Self-Attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries 为什么自我关注是序列对序列问题的自然表现?从对称角度看问题

3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-06-01 DOI: 10.4208/jml.221206

Chao Ma and Lexing Ying null

引用次数: 0

Selective inference for k-means clustering. k-means 聚类的选择性推理。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-05-01

Yiqun T Chen, Daniela M Witten

引用次数: 0

RNN-Attention Based Deep Learning for Solving Inverse Boundary Problems in Nonlinear Marshak Waves 基于rnn -注意力的深度学习求解非线性马沙克波反边界问题

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2023-04-01 DOI: 10.4208/jml.221209

Di Zhao, Weiming Li, Wengu Chen, Peng Song, and Han Wang null

{"title":"RNN-Attention Based Deep Learning for Solving Inverse Boundary Problems in Nonlinear Marshak Waves","authors":"Di Zhao, Weiming Li, Wengu Chen, Peng Song, and Han Wang null","doi":"10.4208/jml.221209","DOIUrl":"https://doi.org/10.4208/jml.221209","url":null,"abstract":". Radiative transfer, described by the radiative transfer equation (RTE), is one of the dominant energy exchange processes in the inertial conﬁnement fusion (ICF) experiments. The Marshak wave problem is an important benchmark for time-dependent RTE. In this work, we present a neural network architecture termed RNN-attention deep learning (RADL) as a surrogate model to solve the inverse boundary problem of the nonlinear Marshak wave in a data-driven fashion. We train the surrogate model by numerical simulation data of the forward problem, and then solve the inverse problem by minimizing the distance between the target solution and the surrogate predicted solution concerning the boundary condition. This minimization is made efﬁcient because the surrogate model by-passes the expensive numerical solution, and the model is differentiable so the gradient-based optimization algorithms are adopted. The effectiveness of our approach is demonstrated by solving the inverse boundary problems of the Marshak wave benchmark in two case studies: where the transport process is modeled by RTE and where it is modeled by its nonlinear diffusion approximation (DA). Last but not least, the importance of using both the RNN and the factor-attention blocks in the RADL model is illustrated, and the data efﬁciency of our model is investigated in this work.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74640699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0