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图 | Takashi Takahashi
题 目:Replicated vector approximate message passing for resampling problem
报告人:Takashi Takahashi
单 位:Tokyo Institute of Technology
时 间:2019-10-06
地 点:中山大学
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Resampling techniques are widely used in statistical inference and ensemble
learning, in which estimators' statistical properties are essential. However, existing
methods are computationally demanding, because repetitions of estimation/learning
via numerical optimization/integral for each resampled data are required.
In this study, we introduce a computationally efficient method to resolve
such problem: replicated vector approximate message passing. This is based
on a combination of the replica method of statistical physics and an accurate approximate
inference algorithm, namely the vector approximate message passing
of information theory. The method provides tractable densities without repeating
estimation/learning, and the densities approximately offer an arbitrary degree
of the estimators' moment in practical time. In the experiment, we apply the
proposed method to the stability selection method, which is commonly used in
variable selection problems. The numerical results show its fast convergence and
high approximation accuracy for problems involving both synthetic and real-world
datasets.Takashi Takahashi is a Ph.D. student in Mathematical and Computing Science at Tokyo Institute of Technology, where he is advised by Yoshiyuki Kabashima. He works in the fields of statistical mechanics of disordered systems and statisitcal inference.
会议简介
2019年10月4日-6日,统计物理与神经计算国际研讨会由中山大学物理学院主办,这是在该校举办的第一届物理,机器学习与计算神经科学交叉的国际会议,会议邀请了这一领域近年来作出杰出贡献的国内外专家参与讨论,并围绕神经网络的计算建模,理论研究,生物机制的最新进展展开。
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