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Variational methods in reinforcement learning

Martin Wainwright (MIT)

Abstract: Reinforcement learning is the study of models and procedures for optimal sequential decision-making under uncertainty.  At its heart lies the Bellman optimality operator, whose unique fixed point specifies an optimal policy and value function.  In this talk, we discuss two classes of variational methods that can be used to obtain approximate solutions with accompanying error guarantees.  For policy evaluation problems based on on-line data, we present Krylov-Bellman boosting, which combines ideas from Krylov methods with non-parametric boosting.  For policy optimization problems based on…

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James-Stein for eigenvectors: reducing the optimization bias in Markowitz portfolios

Lisa Goldberg (UC Berkeley)

Abstract: We identify and reduce bias in the leading sample eigenvector of a high-dimensional covariance matrix of correlated variables. Our analysis illuminates how error in an estimated covariance matrix corrupts optimization. It may be applicable in finance, machine learning and genomics. Biography: Lisa Goldberg is Head of Research at Aperio and Managing Director at BlackRock.  She is Professor of the Practice of Economics at University of California, Berkeley, where she co-directs the Center for Data Analysis in Risk, an industry partnership that…

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IDSS Celebration

This celebratory event reflects on the impact in research and education the Institute for Data, Systems, and Society has had since its launch in 2015 and explores future opportunities with thought leaders and policy experts. In panels and plenary talks, we will discuss the impact of research areas utilizing the available massive data, in-depth understanding of underlying social and engineering systems, and the investigation of social and institutional behavior to provide answers to critical and complex challenges. For more information,…

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