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空间干扰效应的高维检测AI 解读

Modeling interference effects in high-dimensional settings presents significant challenges due to the complexity of underlying dependence structures. Existing approaches often rely on explicit and homogeneous assumptions, limiting their applicability in real-world scenarios. In this paper, we introduce a novel low-rank and sparse treatment effect model that leverages high-dimensional techniques to identify interference structures without restrictive parametric assumptions.…

流形上热核高斯过程的可扩展贝叶斯推断AI 解读

Abstract We establish a scalable manifold learning method and theory, motivated by the problem of estimating functional magnetic resonance imaging activation manifolds in the Human Connectome Project. Our primary contribution is the development of an efficient estimation technique for heat kernel Gaussian processes in the exponential family model. This approach handles large sample sizes n, preserves the intrinsic geometry of data, and significantly reduces computational…

动态矩阵恢复AI 解读

Matrix recovery from sparse observations is an extensively studied topic emerging in various applications, such as recommendation system and signal processing, which includes the matrix completion and compressed sensing models as special cases. In this article, we propose a general framework for dynamic matrix recovery of low-rank matrices that evolve smoothly over time. We start from the setting that the observations are independent across time, then extend to the setting…

Abstract The aim of this article is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments. We propose a novel temporal/spatio-temporal Varying Coefficient Decision Process model, capable of effectively capturing the evolving treatment effects in situations characterized by temporal and/or spatial dependence. Our methodology encompasses the decomposition of…

广义可加模型的在线平滑回拟合AI 解读

We propose an online smoothing backfitting method for generalized additive models coupled with local linear estimation. The idea can be extended to general nonlinear optimization problems. The strategy is to use an appropriate-order expansion to approximate the nonlinear equations and store the coefficients as sufficient statistics which can be updated in an online manner by the dynamic candidate bandwidth method. We investigate the statistical and algorithmic convergences…