開催日時：2023年8月3日 15:30-14:00

開催場所：545共同研究室およびWeb併用 （※対象は特に限定しない。）

講演者：Bryon Aragam先生（Booth School of Business, University of Chicago）

題目：Optimal neighbourhood selection in structural equation models

概要：
We study the optimal sample complexity of neighbourhood selection in linear structural equation models, and compare this to best subset selection (BSS) for linear models under general design. We show by example that — even when the structure is \emph{unknown} — the existence of underlying structure can reduce the sample complexity of neighbourhood selection. This result is complicated by the possibility of path cancellation, which we study in detail, and show that improvements are still possible in the presence of path cancellation. Finally, we support these theoretical observations with experiments. The proof introduces a modified BSS estimator, called klBSS, and compares its performance to BSS. The analysis of klBSS may also be of independent interest since it applies to arbitrary structured models, not necessarily those induced by a structural equation model. Our results have implications for structure learning in graphical models, which often relies on neighbourhood selection as a subroutine.

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