Robust Bayesian Satisficing

Distributional shifts pose a significant challenge to achieving robustness in contemporary machine learning. To overcome this challenge, robust satisficing (RS) seeks a robust solution to an unspecified distributional shift while achieving a utility above a desired threshold. This paper focuses on the problem of RS in contextual Bayesian optimization when there is a discrepancy between the true and reference distributions of the context. We propose a novel robust Bayesian satisficing algorithm called RoBOS for noisy black-box optimization. Our algorithm guarantees sublinear lenient regret under certain assumptions on the amount of distribution shift. In addition, we define a weaker notion of regret called robust satisficing regret, in which our algorithm achieves a sublinear upper bound independent of the amount of distribution shift. To demonstrate the effectiveness of our method, we apply it to various learning problems and compare it to other approaches, such as distributionally robust optimization

Nejla Aktaş ve Türkiye'de kadın girişimci olmak

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2018.This work is a student project of the Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.The History of Turkey course (HIST200) is a requirement for all Bilkent undergraduates. It is designed to encourage students to work in groups on projects concerning any topic of their choice that relates to the history of Turkey. It is designed as an interactive course with an emphasis on research and the objective of investigating events, chronologically short historical periods, as well as historic representations. Students from all departments prepare and present final projects for examination by a committee, with 10 projects chosen to receive awards.Includes bibliographical references (page 21).by Merve Biçer