About

I am a Ph.D. student in Electrical Engineering at KTH Royal Institute of Technology in Stockholm, Sweden, co-advised by Mikael Skoglund and Tobias Oechtering. My doctoral research is supported by the WASP Graduate School where I am following jointly the Artificial Intelligence and Autonomous System curriculum. Before my academic journey, I completed a double master’s degree in Applied and Computational Mathematics from KTH and École Polytechnique de Louvain and a bachelor’s degree in Applied Mathematics and Electrical engineering at École Polytechnique de Louvain.

I am currently a Visiting Student Researcher at Stanford University under the supervision of Professor Benjamin Van Roy.

My research interest lies at the confluence of Information Theory and Reinforcement Learning. Specifically, I am studying the performance of the Thompson Sampling algorithm for Multi-Armed Bandit Problems and Contextual Bandit Problems.

My CV

Publications

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, and Mikael Skoglund, Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems. Prerprint under review, 2024.
arXiv | pdf

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, and Mikael Skoglund, Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards. Presented at ISIT 2023.
arXiv | pdf | conference pdf

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, and Mikael Skoglund, An Information-Theoretic Analysis of Bayesian Reinforcement Learning. Presented at Allerton 2022.
arXiv | pdf | conference pdf

Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, and Benoit Macq, Optimal intermittent particle filter. IEEE Transactions on Signal Processing 2022
arXiv | pdf | journal pdf

Amaury Gouverneur, Optimal measurement times for particle filtering and its application in mobile tumor tracking. Master Thesis 2022, Prom.: Benoit Macq
dial | pdf

Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, and Benoit Macq, Optimal measurement budget allocation for particle filtering. Presented at ICIP 2020
arXiv | pdf | conference pdf

Teaching

Pattern Recognition and Machine Learning, EQ2341, KTH at KTH - 2020-2024

Deep Neural Networks, EP232U at KTH - Spring 2022

Service

Reviewing service for for EUSIPCO (2022-2023)

Bachelor Thesis Supervision:

• Reza Qorbani and Kevin Pettersson - Investigation of Information-Theoretic Bounds on Generalization Error

Master Thesis Supervision:

• Zhen Tian - Anomaly Detection in Application Logs
• Guangze Shi - Privacy leaks from deep linear networks, Information leak via shared gradients in federated learning systems
• Daniel Pérez - Improving Recommender Engines for Video Streaming Platforms with RNNs and Multivariate Data