第56回統計的機械学習セミナー
第56回統計的機械学習セミナーを下記の通りハイブリッド形式で開催いたします． 関心のある方のご参加をお待ちしております．
日時: 2023年 8月 7日（月）
- 15:00-16:30 Bryon Aragam (Booth School of Business, University of Chicago)
講演
15:00-16:30 Bryon Aragam (Booth School of Business, University of Chicago)
講演タイトル： Optimal Neighbourhood Selection in Structural Equation Models
要旨： We study the optimal sample complexity of neighbourhood selection in linear structural equation models, and compare this to conventional methods such as subset selection and the Lasso. We show by example that-even when the structure is unknown-the existence of underlying structure can reduce the sample complexity of neighbourhood selection. We further show how the rates depend on the effect of path cancellation, closely related to the notion of faithfulness, and that nonetheless the improvement persists even when there is path cancellation. Our results have implications for structure learning in graphical models, which often relies on neighbourhood selection as a subroutine.