Journal of Machine Learning Research最新文献

英文中文

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 Q1 AUTOMATION & CONTROL SYSTEMS

Journal of Machine Learning Research

Pub Date : 2024-01-01

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

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.

策略学习是现实世界中许多学习系统的重要组成部分。策略学习的一个主要挑战是如何有效地适应未知环境或任务。最近，有人建议利用不变条件分布来学习模型，以便更好地泛化到未知环境中。然而，假设整个条件分布不变（我们称之为完全不变）在实践中可能是一个太强的假设。在本文中，我们介绍了完全不变性的一种松弛，称为效应不变性（简称 e-不变性），并证明在合适的假设条件下，它足以实现零次策略泛化。我们还讨论了一种扩展方法，当我们从测试环境中获得少量样本时，可以利用 e-invariance 实现少次策略泛化。我们的工作没有假设底层因果图，也没有假设数据是由结构因果模型生成的；相反，我们开发了测试程序，直接从数据中测试电子不变量。我们使用模拟数据和移动健康干预数据集展示了实证结果，以证明我们方法的有效性。

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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 Q1 Mathematics

Journal of Machine Learning Research

Pub Date : 2024-01-01

Rina Foygel Barber, Emil Y Sidky

The alternating direction method of multipliers (ADMM) algorithm is a powerful and flexible tool for complex optimization problems of the form $m i n {f (x) + g (y) : A x + B y = c}$ . ADMM exhibits robust empirical performance across a range of challenging settings including nonsmoothness and nonconvexity of the objective functions $f$ and $g$ , 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 $f$ and $g$ are nondifferentiable-and thus outside the scope of existing theory-as well as a simulated CT image reconstruction problem.

交替乘数方向法（ADMM）算法是一种强大而灵活的工具，可用于解决形式为 m i n { f ( x ) + g ( y ) : A x + B y = c } 的复杂优化问题。.ADMM 在目标函数 f 和 g 的非光滑性和非凸性等一系列挑战性设置中表现出稳健的经验性能，为计算机断层扫描 (CT) 成像的图像重建逆问题提供了一种简单而自然的方法。从理论角度来看，现有的非凸环境下的收敛结果一般都假设目标函数中至少有一个分量函数是平滑的。在这项工作中，我们的新理论结果提供了在受限强凸假设下的收敛保证，而不要求平滑性或可微性，同时还允许在需要时近似处理可微项。我们通过一个 f 和 g 都不可微的模拟例子（因此超出了现有理论的范围）以及一个模拟 CT 图像重建问题，对这些理论结果进行了经验验证。

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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 Q1 Mathematics

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 Q1 Mathematics

Journal of Machine Learning Research

Pub Date : 2023-06-01 DOI: 10.4208/jml.230106

Taehee Ko and Xiantao Li

Non-convex loss functions arise frequently in modern machine learning, and for the theoretical analysis of stochastic optimization methods, the presence of non-isolated minima presents a unique challenge that has remained under-explored. In this paper, we study the local convergence of the stochastic gradient descent method to non-isolated global minima. Under mild assumptions, we estimate the probability for the iterations to stay near the minima by adopting the notion of stochastic stability. After establishing such stability, we present the lower bound complexity in terms of various error criteria for a given error tolerance ǫ and a failure probability γ .

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 Q1 Mathematics

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 Q1 Mathematics

Journal of Machine Learning Research

Pub Date : 2023-06-01 DOI: 10.4208/jml.230703

Nilin Abrahamsen and Lin Lin

Explicit antisymmetrization of a neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum physics. However, this procedure is a priori factorially costly to implement, making it impractical for large numbers of particles. The strategy also suffers from a sign problem. Namely, due to near-exact cancellation of positive and negative contributions, the magnitude of the antisymmetrized function may be significantly smaller than before anti-symmetrization. We show that the anti-symmetric projection of a two-layer neural network can be evaluated efficiently, opening the door to using a generic antisymmetric layer as a building block in anti-symmetric neural network Ansatzes. This approximation is effective when the sign problem is controlled, and we show that this property depends crucially the choice of activation function under standard Xavier/He initialization methods. As a consequence, using a smooth activation function requires re-scaling of the neural network weights compared to standard initializations.

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 Q1 Mathematics

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 Q1 Mathematics

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 Q1 Mathematics

Journal of Machine Learning Research

Pub Date : 2023-05-01

Yiqun T Chen, Daniela M Witten

We consider the problem of testing for a difference in means between clusters of observations identified via $k$ -means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. In recent work, Gao et al. (2022) considered a related problem in the context of hierarchical clustering. Unfortunately, their solution is highly-tailored to the context of hierarchical clustering, and thus cannot be applied in the setting of $k$ -means clustering. In this paper, we propose a p-value that conditions on all of the intermediate clustering assignments in the $k$ -means algorithm. We show that the p-value controls the selective Type I error for a test of the difference in means between a pair of clusters obtained using $k$ -means clustering in finite samples, and can be efficiently computed. We apply our proposal on hand-written digits data and on single-cell RNA-sequencing data.

我们考虑的问题是检验通过 k-means 聚类确定的观测数据聚类之间的均值差异。在这种情况下，经典的假设检验会导致 I 类错误率上升。在最近的工作中，Gao 等人（2022 年）考虑了分层聚类背景下的相关问题。遗憾的是，他们的解决方案与分层聚类的背景高度契合，因此无法应用于 k-means 聚类。在本文中，我们提出了一个 p 值，它是 k-means 算法中所有中间聚类分配的条件。我们证明，该 p 值可以控制在有限样本中使用 k-means 聚类对一对聚类的均值差异进行检验时的选择性 I 类错误，并且可以高效计算。我们将我们的建议应用于手写数字数据和单细胞 RNA 序列数据。

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 Q1 Mathematics

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

. 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.

。辐射传递是惯性约束聚变(ICF)实验中主要的能量交换过程之一，用辐射传递方程(RTE)来描述。马沙克波问题是时变RTE的一个重要基准。在这项工作中，我们提出了一种称为rnn -注意力深度学习(RADL)的神经网络架构作为代理模型，以数据驱动的方式解决非线性马沙克波的逆边界问题。我们利用正演问题的数值模拟数据训练代理模型，然后在边界条件下通过最小化目标解与代理预测解之间的距离来求解逆问题。由于替代模型绕过了昂贵的数值解，并且模型是可微的，因此采用了基于梯度的优化算法，从而使这种最小化变得高效。通过在两个案例研究中解决马沙克波基准的逆边界问题，我们的方法的有效性得到了证明:其中输运过程是由RTE建模的，而它是由其非线性扩散近似(DA)建模的。最后，说明了在RADL模型中同时使用RNN和因子注意块的重要性，并对我们的模型的数据效率进行了研究。

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