This paper presents IB-POMCP, a novel algorithm for online planning under partial
observability. Our approach enhances the decision-making process by using estimations of …

Surrogate models have been successfully used in likelihood-free inference to decrease the
number of simulator evaluations. The current state-of-the-art performance for this task has …

The kernelized bandit problem is a theoretically justified framework and has solid
applications to various fields. Recently, there is a growing interest in generalizing the …

We consider the regret minimization problem in reinforcement learning (RL) in the episodic
setting. In many real-world RL environments, the state and action spaces are continuous or …

We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of
Bayesian optimization problems where the integrated feedback is given via a conditional …

We consider the regret minimization problem in reinforcement learning (RL) in the episodic
setting. In many real-world RL environments, the state and action spaces are continuous or …

Motivated by applications, we consider here new operator theoretic approaches to
Conditional mean embeddings (CME). Our present results combine a spectral analysis …

The problem of sequentially maximizing the expectation of a function seeks to maximize the
expected value of a function of interest without having direct control on its features. Instead …

This thesis thoroughly explores the integration of statistical and reinforcement learning
techniques, aiming to provide fresh perspectives and solutions for enhancing the current …

This paper presents IB-POMCP, a novel algorithm for online planning under partial
observability. Our approach enhances the decision-making process by using estimations of …