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 pursuing the joint curriculum in Artificial Intelligence and Autonomous Systems.

Before starting my Ph.D., 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 from École Polytechnique de Louvain.

In 2024, I had the opportunity to work as a Visiting Student Researcher at Stanford University under the supervision of Professor Benjamin Van Roy.

My research interests lie at the intersection of reinforcement learning and information theory. Specifically, I study the performance of the Thompson Sampling algorithm in bandit problems.

I welcome discussions with anyone interested in topics in information theory and machine learning. Feel free to connect!

My CV

Publications (Chronological)

Amaury Gouverneur, Tobias J. Oechtering, and Mikael Skoglund, Refined PAC-Bayes Bounds for Offline Bandits
Submitted to ISIT 2025 | pdf

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, and Mikael Skoglund, An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits
Presented at NeurIPS 2024 Workshop on "Bayesian Decision-making and Uncertainty" | pdf

Raghav Bongole, Amaury Gouverneur, Tobias J. Oechtering, and Mikael Skoglund, Information-Theoretic Minimax Regret Bounds for Reinforcement Learning Problems
Under review | pdf

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, and Mikael Skoglund, An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces
Accepted at ICASSP 2025 | pdf

Raghav Bongole, Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, and Mikael Skoglund, Information-Theoretic Minimax Regret Bounds for Reinforcement Learning based on Duality
Accepted at ICASSP 2025 | pdf

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, and Mikael Skoglund, Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
Presented at ICML 2024 (FoRLaC Workshop) | 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
Published in 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

Project in Multimedia Processing and Analysis, EQ2445 at KTH - 2024

Machine Learning and Data Science, EQ2415 at KTH - 2024

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

Deep Neural Networks, EP232U at KTH - Spring 2022

Service

Reviewing service for EUSIPCO (2022-2023), ICML 2024, ICASSP 2025, ICLR 2025

WASP Cluster leader for Mathematical Foundations of AI other than ML (2020-2024)

WASP Cluster leader for Sequential Decision-Making and Reinforcement Learning (current)

Bachelor Thesis Supervision:

Master Thesis Supervision