Bou Ammar, Haitham

16 publications

NeurIPS 2024 A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-World Robotics Puze Liu, Jonas Günster, Niklas Funk, Simon Gröger, Dong Chen, Haitham Bou-Ammar, Julius Jankowski, Ante Marić, Sylvain Calinon, Andrej Orsula, Miguel Olivares-Mendez, Hongyi Zhou, Rudolf Lioutikov, Gerhard Neumann, Amarildo Likmeta, Amirhossein Zhalehmehrabi, Thomas Bonenfant, Marcello Restelli, Davide Tateo, Ziyuan Liu, Jan Peters

ICML 2024 Measures of Diversity and Space-Filling Designs for Categorical Data Cedric Malherbe, Emilio Domı́nguez-Sánchez, Merwan Barlier, Igor Colin, Haitham Bou Ammar, Tom Diethe

ICML 2023 Are Random Decompositions All We Need in High Dimensional Bayesian Optimisation? Juliusz Krzysztof Ziomek, Haitham Bou Ammar

JAIR 2022 HEBO: An Empirical Study of Assumptions in Bayesian Optimisation Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan-Rhys Griffiths, Alexandre Max Maraval, Jianye Hao, Jun Wang, Jan Peters, Haitham Bou-Ammar

MLJ 2022 SAMBA: Safe Model-Based & Active Reinforcement Learning Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Amin Abdullah, Aivar Sootla, Jun Wang, Haitham Bou-Ammar

JMLR 2021 Are We Forgetting About Compositional Optimisers in Bayesian Optimisation? Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar

AAAI 2020 Learning to Communicate Implicitly by Actions Zheng Tian, Shihao Zou, Ian Davies, Tim Warr, Lisheng Wu, Haitham Bou-Ammar, Jun Wang

IJCAI 2018 Balancing Two-Player Stochastic Games with Soft Q-Learning Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar

AAAI 2017 Scalable Multitask Policy Gradient Reinforcement Learning Salam El Bsat, Haitham Bou-Ammar, Matthew E. Taylor

IJCAI 2016 Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor

IJCAI 2015 Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning Haitham Bou-Ammar, Eric Eaton, José-Marcio Luna, Paul Ruvolo

AAAI 2015 Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor

AAAI 2014 Online Multi-Task Gradient Temporal-Difference Learning Vishnu Purushothaman Sreenivasan, Haitham Bou-Ammar, Eric Eaton

AAAI 2014 Theory of Cooperation in Complex Social Networks Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Daan Bloembergen, Karl Tuyls, Gerhard Weiss

ECML-PKDD 2013 Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines Haitham Bou-Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens, Karl Tuyls, Gerhard Weiss

IJCAI 2013 Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains Siqi Chen, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss