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Visar resultat 11 - 15 av 46 avhandlingar innehållade ordet regret.
11. Applications of Information Inequalities to Linear Systems : Adaptive Control and Security
Sammanfattning : This thesis considers the application of information inequalities, Cramér-Rao type bounds, based on Fisher information, to linear systems. These tools are used to study the trade-offs between learning and performance in two application areas: adaptive control and control systems security. LÄS MER
12. Inference and Online Learning in Structured Stochastic Systems
Sammanfattning : This thesis contributes to the field of stochastic online learning problems, with a collection of six papers each addressing unique aspects of online learning and inference problems under specific structures. The first four papers focus on exploration and inference problems, uncovering fundamental information-theoretic limits and efficient algorithms under various structures. LÄS MER
13. Age Differences in Experience and Regulation of Affect
Sammanfattning : The overall aim of the thesis is to investigate differences in how younger and older adults view and control affect. Study I and Study II investigate how participants view their happiness and what factors influence their perception of happiness. In Study I we found weak negative association between age and happiness. LÄS MER
14. Efficient Online Learning under Bandit Feedback
Sammanfattning : In this thesis we address the multi-armed bandit (MAB) problem with stochastic rewards and correlated arms. Particularly, we investigate the case when the expected rewards are a Lipschitz function of the arm and extend these results to bandits with arbitrary structure that is known to the decision maker. LÄS MER
15. Reinforcement Learning and Optimal Adaptive Control for Structured Dynamical Systems
Sammanfattning : In this thesis, we study the related problems of reinforcement learning and optimal adaptive control, specialized to specific classes of stochastic and structured dynamical systems. By stochastic, we mean systems that are unknown to the decision maker and evolve according to some probabilistic law. LÄS MER