Geovani Rizk

Expertise Distributed Machine Learning, Robustness, Safety, Optimization, Multi-Agent, Bandits in Graphs I am a postdoctoral researcher at école Polytechnique Fédérale de Lausanne since 2022, where I work with Rachid Guerraoui on robustness of distributed machine learning algorithms. From 2018 to 2022, I completed my Ph.D. in computer science at Université Paris Dauphine-PSL on multi-agent bandits in graphs under the supervision of Yann Chevaleyre, Rida Laraki, Igor Colin, and Albert Thomas.Pre-prints Overcoming the Challenges of Batch Normalization in Federated LearningR Guerraoui, R Pinot, G Rizk, J Stephan, F Taiani & Preprint, Under review (2024)Boosting Robustness by Clipping Gradients in Distributed LearningY Allouah, R Guerraoui, N Gupta, A Jellouli, G Rizk, J Stephan & Preprint, Under review (2024)Publications Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local UpdatesY Allouah, S Farhadkhani, R Guerraoui, N Gupta, R Pinot, G Rizk, S Voitovych & ICML (2024)Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data HeterogeneityY Allouah, R Guerraoui, N Gupta, R Pinot, G. Rizk & NeurIPS (2023)Stochastic Graphical Bilinear BanditsG Rizk & Ph.D. Thesis (2022)An α-No-Regret Algorithm For Graphical Bilinear Bandits.G Rizk, A Thomas, I Colin, R Laraki, Y Chevaleyre & NeurIPS (2022)Best Arm Identification In Graphical Bilinear Bandits.G Rizk, A Thomas, I Colin, R Laraki, Y Chevaleyre & ICML (2021)Randomization Matters. How To Defend Against Strong Adversarial Attacks.R Pinot, R Ettedgui, G Rizk, Y Chevaleyre, J Atif & ICML (2020)Refined Bounds For Randomized Experimental Design.G Rizk, A Thomas, I Colin, M Draief & NeurIPS Workshop, ML with Guarantees (2019)