Learning-Based Robust Optimization: Procedures and Statistical Guarantees

Portfolio Optimization Based on Robust Estimation Procedures

Robust portfolio optimization with derivative insurance guarantees

Robust portfolio optimization aims to maximize the worst-case portfolio return given that the asset returns are allowed to vary within a prescribed uncertainty set. If the uncertainty set is not too large, the resulting portfolio performs well under normal market conditions. However, its performance may substantially degrade in the presence of market crashes, that is, if the asset returns mater...

Multiple Optimality Guarantees in Statistical Learning

Multiple Optimality Guarantees in Statistical Learning by John C Duchi Doctor of Philosophy in Computer Science and the Designated Emphasis in Communication, Computation, and Statistics University of California, Berkeley Professor Michael I. Jordan, Co-chair Professor Martin J. Wainwright, Co-chair Classically, the performance of estimators in statistical learning problems is measured in terms ...

Incremental Learning-to-Learn with Statistical Guarantees

In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution. In contrast to previous work on batch learning-tolearn, we consider a scenario where tasks are presented sequentially and the algorithm needs to adapt incrementally to improve its performance on future tasks. Key to this setting is for the algorithm to ra...

Adaptive Learning with Robust Generalization Guarantees

The traditional notion of generalization—i.e., learning a hypothesis whose empirical error is close to its true error—is surprisingly brittle. As has recently been noted [DFH+15b], even if several algorithms have this guarantee in isolation, the guarantee need not hold if the algorithms are composed adaptively. In this paper, we study three notions of generalization— increasing in strength—that...