Haitham Bou-Ammar

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 83

Top Venues

Publications

Christopher E. Mower, Yuhui Wan, Hongzhan Yu, Antoine Grosnit, Jonas Gonzalez-Billandon, Matthieu Zimmer, Jinlong Wang, Xinyu Zhang, Yao Zhao, Anbang Zhai, Puze Liu, Davide Tateo, Cesar Cadena, Marco Hutter, Jan Peters, Guangjian Tian, Yuzheng Zhuang, Kun Shao, Xingyue Quan, Jianye Hao, Jun Wang, Haitham Bou-Ammar
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning. CoRR (2024)
Shyam Sundhar Ramesh, Yifan Hu, Iason Chaimalas, Viraj Mehta, Pier Giuseppe Sessa, Haitham Bou-Ammar, Ilija Bogunovic
Group Robust Preference Optimization in Reward-free RLHF. CoRR (2024)
Piotr Kicki, Puze Liu, Davide Tateo, Haitham Bou-Ammar, Krzysztof Walas, Piotr Skrzypczynski, Jan Peters
Fast Kinodynamic Planning on the Constraint Manifold With Deep Neural Networks. IEEE Trans. Robotics 40 (2024)
Yao Zhao, Tao Wu, Yijie Zhu, Xiang Lu, Jun Wang, Haitham Bou-Ammar, Xinyu Zhang, Peng Du
ZSL-RPPO: Zero-Shot Learning for Quadrupedal Locomotion in Challenging Terrains using Recurrent Proximal Policy Optimization. CoRR (2024)
Zafeirios Fountas, Martin Benfeghoul, Adnan Oomerjee, Fenia Christopoulou, Gerasimos Lampouras, Haitham Bou-Ammar, Jun Wang
Human-like Episodic Memory for Infinite Context LLMs. CoRR (2024)
Puze Liu, Haitham Bou-Ammar, Jan Peters, Davide Tateo
Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications. CoRR (2024)
Adam X. Yang, Maxime Robeyns, Thomas Coste, Jun Wang, Haitham Bou-Ammar, Laurence Aitchison
Bayesian Reward Models for LLM Alignment. CoRR (2024)
Xihan Li, Xiang Chen, Rasul Tutunov, Haitham Bou-Ammar, Lei Wang, Jun Wang
Online PCA in Converging Self-consistent Field Equations. NeurIPS (2023)
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer, Thomas Coste, Zhihao Wu, Jingxuan Chen, Khyati Khandelwal, James Doran, Xidong Feng, Jiacheng Liu, Zheng Xiong, Yicheng Luo, Jianye Hao, Kun Shao, Haitham Bou-Ammar, Jun Wang
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning. CoRR (2023)
Vahan Arsenyan, Antoine Grosnit, Haitham Bou-Ammar
Contextual Causal Bayesian Optimisation. CoRR (2023)
Juliusz Krysztof Ziomek, Haitham Bou-Ammar
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? ICML (2023)
Desong Du, Shaohang Han, Naiming Qi, Haitham Bou-Ammar, Jun Wang, Wei Pan
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions. ICRA (2023)
Piotr Kicki, Puze Liu, Davide Tateo, Haitham Bou-Ammar, Krzysztof Walas, Piotr Skrzypczynski, Jan Peters
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks. CoRR (2023)
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. CoRR (2023)
Juliusz Ziomek, Haitham Bou-Ammar
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? CoRR (2023)
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. NeurIPS (2023)
Kamil Dreczkowski, Antoine Grosnit, Haitham Bou-Ammar
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. CoRR (2023)
Kamil Dreczkowski, Antoine Grosnit, Haitham Bou-Ammar
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. NeurIPS (2023)
Antoine Grosnit, Matthieu Zimmer, Rasul Tutunov, Xing Li, Lei Chen, Fan Yang, Mingxuan Yuan, Haitham Bou-Ammar
Lightweight Structural Choices Operator for Technology Mapping. DAC (2023)
Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, Haitham Bou-Ammar
Why Can Large Language Models Generate Correct Chain-of-Thoughts? CoRR (2023)
Alexander I. Cowen-Rivers, Philip John Gorinski, Aivar Sootla, Asif Khan, Furui Liu, Jun Wang, Jan Peters, Haitham Bou-Ammar
Structured Q-learning For Antibody Design. CoRR (2022)
Hang Ren, Aivar Sootla, Taher Jafferjee, Junxiao Shen, Jun Wang, Haitham Bou-Ammar
Reinforcement Learning in Presence of Discrete Markovian Context Evolution. CoRR (2022)
Aivar Sootla, Alexander Imani Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David Mguni, Jun Wang, Haitham Bou-Ammar
SAUTE RL: Almost Surely Safe Reinforcement Learning Using State Augmentation. CoRR (2022)
Alexander Imani Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Amin Abdullah, Aivar Sootla, Jun Wang, Haitham Bou-Ammar
SAMBA: safe model-based & active reinforcement learning. Mach. Learn. 111 (1) (2022)
Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou-Ammar
Enhancing Safe Exploration Using Safety State Augmentation. CoRR (2022)
Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Rasul Tutunov, Jun Wang, Haitham Bou-Ammar
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates. CoRR (2022)
Le Cong Dinh, Stephen Marcus McAleer, Zheng Tian, Nicolas Perez Nieves, Oliver Slumbers, David Henry Mguni, Jun Wang, Haitham Bou-Ammar, Yaodong Yang
Online Double Oracle. Trans. Mach. Learn. Res. 2022 (2022)
Cédric Malherbe, Antoine Grosnit, Rasul Tutunov, Haitham Bou-Ammar, Jun Wang
Optimistic Tree Searches for Combinatorial Black-Box Optimization. NeurIPS (2022)
Xihan Li, Xiang Chen, Rasul Tutunov, Haitham Bou-Ammar, Lei Wang, Jun Wang
Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations. CoRR (2022)
Tianyu Ren, Alexander Imani Cowen-Rivers, Haitham Bou-Ammar, Jan Peters
Learning Geometric Constraints in Task and Motion Planning. CoRR (2022)
Asif Khan, Alexander Imani Cowen-Rivers, Derrick-Goh-Xin Deik, Antoine Grosnit, Kamil Dreczkowski, Philippe A. Robert, Victor Greiff, Rasul Tutunov, Dany Bou-Ammar, Jun Wang, Haitham Bou-Ammar
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation. CoRR (2022)
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
HEBO: An Empirical Study of Assumptions in Bayesian Optimisation. J. Artif. Intell. Res. 74 (2022)
Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar
BOiLS: Bayesian Optimisation for Logic Synthesis. DATE (2022)
Hang Ren, Aivar Sootla, Taher Jafferjee, Junxiao Shen, Jun Wang, Haitham Bou-Ammar
Reinforcement Learning in Presence of Discrete Markovian Context Evolution. ICLR (2022)
Antoine Grosnit, Alexander Imani Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar
Are We Forgetting about Compositional Optimisers in Bayesian Optimisation? J. Mach. Learn. Res. 22 (2021)
Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters
Efficient and Reactive Planning for High Speed Robot Air Hockey. CoRR (2021)
Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar
BOiLS: Bayesian Optimisation for Logic Synthesis. CoRR (2021)
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander Imani Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning. CoRR (2021)
Vincent Moens, Aivar Sootla, Haitham Bou-Ammar, Jun Wang
Implicit Variational Conditional Sampling with Normalizing Flows. CoRR (2021)
Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters
Robot Reinforcement Learning on the Constraint Manifold. CoRL (2021)
Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. CoRR (2021)
Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. AAMAS (2021)
Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters
Efficient and Reactive Planning for High Speed Robot Air Hockey. IROS (2021)
Le Cong Dinh, Yaodong Yang, Zheng Tian, Nicolas Perez Nieves, Oliver Slumbers, David Henry Mguni, Haitham Bou-Ammar, Jun Wang
Online Double Oracle. CoRR (2021)
Rasul Tutunov, Minne Li, Jun Wang, Haitham Bou-Ammar
Compositional ADAM: An Adaptive Compositional Solver. CoRR (2020)
Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat M. Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander Imani Cowen-Rivers, Zheng Tian, Daniel Palenicek, Haitham Bou-Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving. CoRR (2020)
Antoine Grosnit, Alexander Imani Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? CoRR (2020)
Alexander Imani Cowen-Rivers, Wenlong Lyu, Zhi Wang, Rasul Tutunov, Jianye Hao, Jun Wang, Haitham Bou-Ammar
HEBO: Heteroscedastic Evolutionary Bayesian Optimisation. CoRR (2020)
Zheng Tian, Shihao Zou, Ian Davies, Tim Warr, Lisheng Wu, Haitham Bou-Ammar, Jun Wang
Learning to Communicate Implicitly by Actions. AAAI (2020)
Yaodong Yang, Rasul Tutunov, Phu Sakulwongtana, Haitham Bou-Ammar
αα-Rank: Practically Scaling α-Rank through Stochastic Optimisation. AAMAS (2020)
Minne Li, Lisheng Wu, Jun Wang, Haitham Bou-Ammar
Multi-View Reinforcement Learning. NeurIPS (2019)
Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie
Distributed Newton Method for Large-Scale Consensus Optimization. IEEE Trans. Autom. Control. 64 (10) (2019)
Minne Li, Lisheng Wu, Haitham Bou-Ammar, Jun Wang
Multi-View Reinforcement Learning. CoRR (2019)
Mohammed Amin Abdullah, Hang Ren, Haitham Bou-Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, Jun Wang
Wasserstein Robust Reinforcement Learning. CoRR (2019)
Victor Gabillon, Rasul Tutunov, Michal Valko, Haitham Bou-Ammar
Derivative-Free & Order-Robust Optimisation. CoRR (2019)
Garrett Andersen, Peter Vrancx, Haitham Bou-Ammar
Learning High-level Representations from Demonstrations. CoRR (2018)
Christopher Amato, Haitham Bou-Ammar, Elizabeth F. Churchill, Erez Karpas, Takashi Kido, Mike Kuniavsky, William F. Lawless, Francesca Rossi, Frans A. Oliehoek, Stephen Russell, Keiki Takadama, Siddharth Srivastava, Karl Tuyls, Philip van Allen, Kristen Brent Venable, Peter Vrancx, Shiqi Zhang
Reports on the 2018 AAAI Spring Symposium Series. AI Mag. 39 (4) (2018)
Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
Balancing Two-Player Stochastic Games with Soft Q-Learning. IJCAI (2018)
Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
Balancing Two-Player Stochastic Games with Soft Q-Learning. CoRR (2018)
Felix Leibfried, Rasul Tutunov, Peter Vrancx, Haitham Bou-Ammar
Model-Based Stabilisation of Deep Reinforcement Learning. CoRR (2018)
Rasul Tutunov, Dongho Kim, Haitham Bou-Ammar
Distributed Multitask Reinforcement Learning with Quadratic Convergence. NeurIPS (2018)
Rasul Tutunov, Julia El Zini, Haitham Bou-Ammar, Ali Jadbabaie
Distributed lifelong reinforcement learning with sub-linear regret. CDC (2017)
Felix Leibfried, Jordi Grau-Moya, Haitham Bou-Ammar
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning. CoRR (2017)
Haitham Bou-Ammar, Mohamad Jaber, Mohamed Nassar
Correctness-by-Learning of Infinite-State Component-Based Systems. FACS (2017)
Decebal Constantin Mocanu, Haitham Bou-Ammar, Luis Puig, Eric Eaton, Antonio Liotta
Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. Pattern Recognit. 69 (2017)
Salam El Bsat, Haitham Bou-Ammar, Matthew E. Taylor
Scalable Multitask Policy Gradient Reinforcement Learning. AAAI (2017)
Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor
Nonconvex Policy Search Using Variational Inequalities. Neural Comput. 29 (10) (2017)
Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor
Scalable lifelong reinforcement learning. Pattern Recognit. 72 (2017)
Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer. IJCAI (2016)
Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss
On the prevalence of hierarchies in social networks. Soc. Netw. Anal. Min. 6 (1) (2016)
Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie
An exact distributed newton method for reinforcement learning. CDC (2016)
Decebal Constantin Mocanu, Haitham Bou-Ammar, Luis Puig, Eric Eaton, Antonio Liotta
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines. CoRR (2016)
Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer. CoRR (2016)
Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie
A Distributed Newton Method for Large Scale Consensus Optimization. CoRR (2016)
Haitham Bou-Ammar, Rasul Tutunov, Eric Eaton
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret. CoRR (2015)
Decebal Constantin Mocanu, Georgios Exarchakos, Haitham Bou-Ammar, Antonio Liotta
Reduced reference image quality assessment via Boltzmann Machines. IM (2015)
Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor
Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment. AAAI (2015)
Decebal Constantin Mocanu, Haitham Bou-Ammar, Dietwig Lowet, Kurt Driessens, Antonio Liotta, Gerhard Weiss, Karl Tuyls
Factored four way conditional restricted Boltzmann machines for activity recognition. Pattern Recognit. Lett. 66 (2015)
Haitham Bou-Ammar, Eric Eaton, José-Marcio Luna, Paul Ruvolo
Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning. IJCAI (2015)
Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss
On the Skewed Degree Distribution of Hierarchical Networks. ASONAM (2015)
Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie
A Fast Distributed Solver for Symmetric Diagonally Dominant Linear Equations. CoRR (2015)
Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie
Fast, accurate second order methods for network optimization. CDC (2015)
Haitham Bou-Ammar, Rasul Tutunov, Eric Eaton
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret. ICML (2015)
Siqi Chen, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss
Transfer for Automated Negotiation. Künstliche Intell. 28 (1) (2014)
Elena Mocanu, Decebal Constantin Mocanu, Haitham Bou-Ammar, Zoran Zivkovic, Antonio Liotta, Evgueni N. Smirnov
Inexpensive user tracking using Boltzmann Machines. SMC (2014)
Daan Bloembergen, Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss
Influencing Social Networks: An Optimal Control Study. ECAI (2014)
Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Daan Bloembergen, Karl Tuyls, Gerhard Weiss
Theory of Cooperation in Complex Social Networks. AAAI (2014)
Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Daan Bloembergen, Karl Tuyls, Gerhard Weiss
Evolution of cooperation in arbitrary complex networks. AAMAS (2014)
Vishnu Purushothaman Sreenivasan, Haitham Bou-Ammar, Eric Eaton
Online Multi-Task Gradient Temporal-Difference Learning. AAAI (2014)
Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie, Eric Eaton
On the Degree Distribution of Pólya Urn Graph Processes. CoRR (2014)
Haitham Bou-Ammar, Siqi Chen, Karl Tuyls, Gerhard Weiss
Automated Transfer for Reinforcement Learning Tasks. Künstliche Intell. 28 (1) (2014)
Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor
Online Multi-Task Learning for Policy Gradient Methods. ICML (2014)
Siqi Chen, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss
Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains. IJCAI (2013)
Mohammad Chami, Haitham Bou-Ammar, Holger Voos, Karl Tuyls, Gerhard Weiss
Swarm-based evaluation of nonparametric SysML mechatronics system design. ICM (2013)
Haitham Bou-Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens, Karl Tuyls, Gerhard Weiss
Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines. ECML/PKDD (2) (2013)
Siqi Chen, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss
Optimizing complex automated negotiation using sparse pseudo-input gaussian processes. AAMAS (2013)
Shuang Zhou, Evgueni N. Smirnov, Haitham Bou-Ammar, Ralf Peeters
Conformity-Based Transfer AdaBoost Algorithm. AIAI (2013)
Haitham Bou-Ammar, Karl Tuyls, Michael Kaisers
Evolutionary Dynamics of Ant Colony Optimization. MATES (2012)
Haitham Bou-Ammar, Karl Tuyls, Matthew E. Taylor, Kurt Driessens, Gerhard Weiss
Reinforcement learning transfer via sparse coding. AAMAS (2012)
Haitham Bou-Ammar, Matthew E. Taylor
Reinforcement Learning Transfer via Common Subspaces. ALA (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Haitham Bou-Ammar