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Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery
This thesis aims to study some of the mathematical challenges that arise in the analysis of statistical sequential decision-making algorithms for postoperative patients follow-up.
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study
We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery.
Risk-aware linear bandits with convex loss
In decision-making problems such as the multi-armed bandit, an agent learns sequentially by optimizing a certain feedback.
Bregman Deviations of Generic Exponential Families
For the practitioner, we instantiate this novel bound to several classical families, e. g., Gaussian, Bernoulli, Exponential, Weibull, Pareto, Poisson and Chi-square yielding explicit forms of the confidence sets and the Bregman information gain.
From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits
The stochastic multi-arm bandit problem has been extensively studied under standard assumptions on the arm's distribution (e. g bounded with known support, exponential family, etc).
From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits
The stochastic multi-arm bandit problem has been extensively studied under standard assumptions on the arm's distribution (e. g bounded with known support, exponential family, etc).
