Dear all,
This is a gentle reminder that tomorrow we have Nikita Zhivotovskiy speaking in the Statistics and Machine Learning Thematic Seminar. Nikita has done excellent work both in statistics and machine learning, so it is highly recommended to come meet him.
*Nikita Zhivotovskiy *(UC Berkeley, Department of Statistics, https://sites.google.com/view/nikitazhivotovskiy/)
*Friday July 7*, 15h00-16h00 In person, at the University of Amsterdam Location: Science Park 904, Room A1.04
*Sharper Risk Bounds for Statistical Aggregation
*In this talk, we take a fresh look at the classical results in the theory of statistical aggregation, focusing on the transition from global complexity to a more manageable local one. The goal of aggregation is to combine several base predictors to achieve a prediction nearly as accurate as the best one. This flexible approach operates without any assumptions on the structure of the class or the nature of the target. Though aggregation is studied in both sequential and statistical settings, each with their unique differences, they both traditionally use the same "global" complexity measure. Our discussion will highlight the lesser-known PAC-Bayes localization method used in our proofs, allowing us to prove a localized version of a classical bound for the exponential weights estimator by Leung and Barron, and a deviation-optimal localized bound for the Q-aggregation estimator. Furthermore, we will explore the links between our work and ridge regression. Joint work with Jaouad Mourtada and Tomas Vaškevičius. * *
Seminar organizers: Tim van Erven Botond Szabo
https://mschauer.github.io/StructuresSeminar/