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2020 – today
- 2024
[j14]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: 277-297 (2024)- [c51]Cédric Malherbe, Emilio Domínguez-Sánchez, Merwan Barlier, Igor Colin, Haitham Bou-Ammar, Tom Diethe:
Measures of diversity and space-filling designs for categorical data. ICML 2024 - [i48]Adam X. Yang, Maxime Robeyns, Thomas Coste, Jun Wang, Haitham Bou-Ammar, Laurence Aitchison:
Bayesian Reward Models for LLM Alignment. CoRR abs/2402.13210 (2024) - [i47]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 abs/2403.01928 (2024) - [i46]Puze Liu, Haitham Bou-Ammar, Jan Peters, Davide Tateo:
Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications. CoRR abs/2404.09080 (2024) - [i45]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 abs/2405.20304 (2024) - [i44]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 abs/2406.19741 (2024) - [i43]Zafeirios Fountas, Martin Benfeghoul, Adnan Oomerjee, Fenia Christopoulou, Gerasimos Lampouras, Haitham Bou-Ammar, Jun Wang:
Human-like Episodic Memory for Infinite Context LLMs. CoRR abs/2407.09450 (2024) - [i42]Dimitrios Tsaras, Antoine Grosnit, Lei Chen, Zhiyao Xie, Haitham Bou-Ammar, Mingxuan Yuan:
ShortCircuit: AlphaZero-Driven Circuit Design. CoRR abs/2408.09858 (2024) - 2023
- [c50]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: 1-6 - [c49]Juliusz Krysztof Ziomek, Haitham Bou-Ammar:
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? ICML 2023: 43347-43368 - [c48]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: 9442-9448 - [c47]Xihan Li, Xiang Chen, Rasul Tutunov, Haitham Bou-Ammar, Lei Wang, Jun Wang:
Online PCA in Converging Self-consistent Field Equations. NeurIPS 2023 - [c46]Kamil Dreczkowski, Antoine Grosnit, Haitham Bou-Ammar:
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. NeurIPS 2023 - [c45]Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar:
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. NeurIPS 2023 - [i41]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 abs/2301.04330 (2023) - [i40]Vahan Arsenyan, Antoine Grosnit, Haitham Bou-Ammar:
Contextual Causal Bayesian Optimisation. CoRR abs/2301.12412 (2023) - [i39]Juliusz Ziomek, Haitham Bou-Ammar:
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? CoRR abs/2301.12844 (2023) - [i38]Desong Du, Shaohang Han, Naiming Qi, Haitham Bou-Ammar, Jun Wang, Wei Pan:
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions. CoRR abs/2305.09793 (2023) - [i37]Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou-Ammar:
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes. CoRR abs/2305.15930 (2023) - [i36]Kamil Dreczkowski, Antoine Grosnit, Haitham Bou-Ammar:
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization. CoRR abs/2306.09803 (2023) - [i35]Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, Haitham Bou-Ammar:
Why Can Large Language Models Generate Correct Chain-of-Thoughts? CoRR abs/2310.13571 (2023) - [i34]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 abs/2312.14878 (2023) - 2022
- [j13]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: 1269-1349 (2022) - [j12]Alexander I. 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): 173-203 (2022) - [j11]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) - [c44]Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar:
BOiLS: Bayesian Optimisation for Logic Synthesis. DATE 2022: 1193-1196 - [c43]Hang Ren, Aivar Sootla, Taher Jafferjee, Junxiao Shen, Jun Wang, Haitham Bou-Ammar:
Reinforcement Learning in Presence of Discrete Markovian Context Evolution. ICLR 2022 - [c42]Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David Henry Mguni, Jun Wang, Haitham Ammar:
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation. ICML 2022: 20423-20443 - [c41]Cédric Malherbe, Antoine Grosnit, Rasul Tutunov, Haitham Bou-Ammar, Jun Wang:
Optimistic Tree Searches for Combinatorial Black-Box Optimization. NeurIPS 2022 - [c40]Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou-Ammar:
Enhancing Safe Exploration Using Safety State Augmentation. NeurIPS 2022 - [i33]Tianyu Ren, Alexander Imani Cowen-Rivers, Haitham Bou-Ammar, Jan Peters:
Learning Geometric Constraints in Task and Motion Planning. CoRR abs/2201.09612 (2022) - [i32]Mohammad Asif Khan, Alexander I. 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 abs/2201.12570 (2022) - [i31]Xihan Li, Xiang Chen, Rasul Tutunov, Haitham Bou-Ammar, Lei Wang, Jun Wang:
Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations. CoRR abs/2202.01388 (2022) - [i30]Hang Ren, Aivar Sootla, Taher Jafferjee, Junxiao Shen, Jun Wang, Haitham Bou-Ammar:
Reinforcement Learning in Presence of Discrete Markovian Context Evolution. CoRR abs/2202.06557 (2022) - [i29]Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David Mguni, Jun Wang, Haitham Bou-Ammar:
SAUTE RL: Almost Surely Safe Reinforcement Learning Using State Augmentation. CoRR abs/2202.06558 (2022) - [i28]Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Rasul Tutunov, Jun Wang, Haitham Bou-Ammar:
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates. CoRR abs/2205.13902 (2022) - [i27]Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou-Ammar:
Enhancing Safe Exploration Using Safety State Augmentation. CoRR abs/2206.02675 (2022) - [i26]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 abs/2209.04698 (2022) - 2021
- [j10]Antoine Grosnit, Alexander I. 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: 160:1-160:78 (2021) - [c39]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: 51-56 - [c38]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Robot Reinforcement Learning on the Constraint Manifold. CoRL 2021: 1357-1366 - [c37]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Efficient and Reactive Planning for High Speed Robot Air Hockey. IROS 2021: 586-593 - [i25]Vincent Moens, Hang Ren, Alexandre Maraval, Rasul Tutunov, Jun Wang, Haitham Ammar:
Efficient Semi-Implicit Variational Inference. CoRR abs/2101.06070 (2021) - [i24]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 abs/2102.07659 (2021) - [i23]Le Cong Dinh, Yaodong Yang, Zheng Tian, Nicolas Perez Nieves, Oliver Slumbers, David Henry Mguni, Haitham Bou-Ammar, Jun Wang:
Online Double Oracle. CoRR abs/2103.07780 (2021) - [i22]Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I. 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 abs/2106.03609 (2021) - [i21]Vincent Moens, Aivar Sootla, Haitham Bou-Ammar, Jun Wang:
Implicit Variational Conditional Sampling with Normalizing Flows. CoRR abs/2107.02474 (2021) - [i20]Puze Liu, Davide Tateo, Haitham Bou-Ammar, Jan Peters:
Efficient and Reactive Planning for High Speed Robot Air Hockey. CoRR abs/2107.06140 (2021) - [i19]Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar:
BOiLS: Bayesian Optimisation for Logic Synthesis. CoRR abs/2111.06178 (2021) - 2020
- [c36]Zheng Tian, Shihao Zou, Ian Davies, Tim Warr, Lisheng Wu, Haitham Bou-Ammar, Jun Wang:
Learning to Communicate Implicitly by Actions. AAAI 2020: 7261-7268 - [c35]Haitham Ammar, Victor Gabillon, Rasul Tutunov, Michal Valko:
Derivative-Free & Order-Robust Optimisation. AISTATS 2020: 2293-2303 - [c34]Yaodong Yang, Rasul Tutunov, Phu Sakulwongtana, Haitham Bou-Ammar:
αα-Rank: Practically Scaling α-Rank through Stochastic Optimisation. AAMAS 2020: 1575-1583 - [c33]Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Zhengbang Zhu, Yihan Ni, Nhat M. Nguyen, Mohamed Elsayed, Haitham Ammar, Alexander I. Cowen-Rivers, Sanjeevan Ahilan, Zheng Tian, Daniel Palenicek, Kasra Rezaee, Peyman Yadmellat, Kun Shao, Dong Chen, Baokuan Zhang, Hongbo Zhang, Jianye Hao, Wulong Liu, Jun Wang:
SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving. CoRL 2020: 264-285 - [i18]Rasul Tutunov, Minne Li, Jun Wang, Haitham Bou-Ammar:
Compositional ADAM: An Adaptive Compositional Solver. CoRR abs/2002.03755 (2020) - [i17]Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Amin Abdullah, Aivar Sootla, Jun Wang, Haitham Ammar:
SAMBA: Safe Model-Based & Active Reinforcement Learning. CoRR abs/2006.09436 (2020) - [i16]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 I. 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 abs/2010.09776 (2020) - [i15]Alexander I. Cowen-Rivers, Wenlong Lyu, Zhi Wang, Rasul Tutunov, Jianye Hao, Jun Wang, Haitham Bou-Ammar:
HEBO: Heteroscedastic Evolutionary Bayesian Optimisation. CoRR abs/2012.03826 (2020) - [i14]Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar:
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? CoRR abs/2012.08240 (2020)
2010 – 2019
- 2019
- [j9]Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie:
Distributed Newton Method for Large-Scale Consensus Optimization. IEEE Trans. Autom. Control. 64(10): 3983-3994 (2019) - [c32]Minne Li, Lisheng Wu, Jun Wang, Haitham Bou-Ammar:
Multi-View Reinforcement Learning. NeurIPS 2019: 1418-1429 - [i13]Mohammed Amin Abdullah, Hang Ren, Haitham Bou-Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, Jun Wang:
Wasserstein Robust Reinforcement Learning. CoRR abs/1907.13196 (2019) - [i12]Victor Gabillon, Rasul Tutunov, Michal Valko, Haitham Bou-Ammar:
Derivative-Free & Order-Robust Optimisation. CoRR abs/1910.04034 (2019) - [i11]Minne Li, Lisheng Wu, Haitham Bou-Ammar, Jun Wang:
Multi-View Reinforcement Learning. CoRR abs/1910.08285 (2019) - 2018
- [j8]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): 29-35 (2018) - [c31]Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar:
Balancing Two-Player Stochastic Games with Soft Q-Learning. IJCAI 2018: 268-274 - [c30]Rasul Tutunov, Dongho Kim, Haitham Bou-Ammar:
Distributed Multitask Reinforcement Learning with Quadratic Convergence. NeurIPS 2018: 8921-8930 - [i10]Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar:
Balancing Two-Player Stochastic Games with Soft Q-Learning. CoRR abs/1802.03216 (2018) - [i9]Garrett Andersen, Peter Vrancx, Haitham Bou-Ammar:
Learning High-level Representations from Demonstrations. CoRR abs/1802.06604 (2018) - [i8]Felix Leibfried, Rasul Tutunov, Peter Vrancx, Haitham Bou-Ammar:
Model-Based Stabilisation of Deep Reinforcement Learning. CoRR abs/1809.01906 (2018) - 2017
- [j7]Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor:
Nonconvex Policy Search Using Variational Inequalities. Neural Comput. 29(10): 2800-2824 (2017) - [j6]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: 325-335 (2017) - [j5]Yusen Zhan, Haitham Bou-Ammar, Matthew E. Taylor:
Scalable lifelong reinforcement learning. Pattern Recognit. 72: 407-418 (2017) - [c29]Salam El Bsat, Haitham Bou-Ammar, Matthew E. Taylor:
Scalable Multitask Policy Gradient Reinforcement Learning. AAAI 2017: 1847-1853 - [c28]Rasul Tutunov, Julia El Zini, Haitham Bou-Ammar, Ali Jadbabaie:
Distributed lifelong reinforcement learning with sub-linear regret. CDC 2017: 2254-2259 - [c27]Haitham Bou-Ammar, Mohamad Jaber, Mohamed Nassar:
Correctness-by-Learning of Infinite-State Component-Based Systems. FACS 2017: 162-178 - [i7]Felix Leibfried, Jordi Grau-Moya, Haitham Bou-Ammar:
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning. CoRR abs/1708.01867 (2017) - 2016
- [j4]Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss:
On the prevalence of hierarchies in social networks. Soc. Netw. Anal. Min. 6(1): 58:1-58:16 (2016) - [c26]Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie:
An exact distributed newton method for reinforcement learning. CDC 2016: 1003-1008 - [c25]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: 2315-2321 - [i6]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 abs/1604.03986 (2016) - [i5]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 abs/1604.05865 (2016) - [i4]Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie:
A Distributed Newton Method for Large Scale Consensus Optimization. CoRR abs/1606.06593 (2016) - 2015
- [j3]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: 100-108 (2015) - [c24]Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor:
Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment. AAAI 2015: 2504-2510 - [c23]Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss:
On the Skewed Degree Distribution of Hierarchical Networks. ASONAM 2015: 298-301 - [c22]Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie:
Fast, accurate second order methods for network optimization. CDC 2015: 706-711 - [c21]Haitham Bou-Ammar, Rasul Tutunov, Eric Eaton:
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret. ICML 2015: 2361-2369 - [c20]Haitham Bou-Ammar, Eric Eaton, José-Marcio Luna, Paul Ruvolo:
Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning. IJCAI 2015: 3345-3351 - [c19]Decebal Constantin Mocanu, Georgios Exarchakos, Haitham Bou-Ammar, Antonio Liotta:
Reduced reference image quality assessment via Boltzmann Machines. IM 2015: 1278-1281 - [i3]Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie:
A Fast Distributed Solver for Symmetric Diagonally Dominant Linear Equations. CoRR abs/1502.03158 (2015) - [i2]Haitham Bou-Ammar, Rasul Tutunov, Eric Eaton:
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret. CoRR abs/1505.05798 (2015) - 2014
- [j2]Haitham Bou-Ammar, Siqi Chen, Karl Tuyls, Gerhard Weiss:
Automated Transfer for Reinforcement Learning Tasks. Künstliche Intell. 28(1): 7-14 (2014) - [j1]Siqi Chen, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss:
Transfer for Automated Negotiation. Künstliche Intell. 28(1): 21-27 (2014) - [c18]Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Daan Bloembergen, Karl Tuyls, Gerhard Weiss:
Theory of Cooperation in Complex Social Networks. AAAI 2014: 1471-1477 - [c17]Vishnu Purushothaman Sreenivasan, Haitham Bou-Ammar, Eric Eaton:
Online Multi-Task Gradient Temporal-Difference Learning. AAAI 2014: 3136-3137 - [c16]Bijan Ranjbar Sahraei, Dean Bloembergen, Haitham Ammar, Karl Tuyls, Gerhard Weiss:
Effects of Evolution on the Emergence of Scale Free Networks. ALIFE 2014: 376-383 - [c15]Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Daan Bloembergen, Karl Tuyls, Gerhard Weiss:
Evolution of cooperation in arbitrary complex networks. AAMAS 2014: 677-684 - [c14]Daan Bloembergen, Bijan Ranjbar Sahraei, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss:
Influencing Social Networks: An Optimal Control Study. ECAI 2014: 105-110 - [c13]Haitham Bou-Ammar, Eric Eaton, Paul Ruvolo, Matthew E. Taylor:
Online Multi-Task Learning for Policy Gradient Methods. ICML 2014: 1206-1214 - [c12]Elena Mocanu, Decebal Constantin Mocanu, Haitham Bou-Ammar, Zoran Zivkovic, Antonio Liotta, Evgueni N. Smirnov:
Inexpensive user tracking using Boltzmann Machines. SMC 2014: 1-6 - [i1]Rasul Tutunov, Haitham Bou-Ammar, Ali Jadbabaie, Eric Eaton:
On the Degree Distribution of Pólya Urn Graph Processes. CoRR abs/1410.8515 (2014) - 2013
- [c11]Siqi Chen, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss:
Optimizing complex automated negotiation using sparse pseudo-input gaussian processes. AAMAS 2013: 707-714 - [c10]Mohammad Chami, Haitham Bou-Ammar, Holger Voos, Karl Tuyls, Gerhard Weiss:
Swarm-based evaluation of nonparametric SysML mechatronics system design. ICM 2013: 436-441 - [c9]Shuang Zhou, Evgueni N. Smirnov, Haitham Bou-Ammar, Ralf Peeters:
Conformity-Based Transfer AdaBoost Algorithm. AIAI 2013: 401-410 - [c8]Siqi Chen, Haitham Bou-Ammar, Karl Tuyls, Gerhard Weiss:
Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains. IJCAI 2013: 69-75 - [c7]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: 449-464 - 2012
- [c6]Haitham Bou-Ammar, Karl Tuyls, Matthew E. Taylor, Kurt Driessens, Gerhard Weiss:
Reinforcement learning transfer via sparse coding. AAMAS 2012: 383-390 - [c5]Haitham Bou-Ammar, Karl Tuyls, Michael Kaisers:
Evolutionary Dynamics of Ant Colony Optimization. MATES 2012: 40-52 - 2011
- [c4]Haitham Bou-Ammar, Matthew E. Taylor:
Reinforcement Learning Transfer via Common Subspaces. ALA 2011: 21-36 - [c3]Haitham Bou-Ammar, Matthew E. Taylor, Karl Tuyls, Gerhard Weiss:
Reinforcement Learning Transfer Using a Sparse Coded Inter-task Mapping. EUMAS 2011: 1-16 - 2010
- [c2]Haitham Bou-Ammar, Holger Voos, Wolfgang Ertel:
Controller Design for Quadrotor UAVs using Reinforcement Learning. CCA 2010: 2130-2135 - [c1]Holger Voos, Haitham Bou-Ammar:
Nonlinear Tracking and Landing Controller for Quadrotor Aerial Robots. CCA 2010: 2136-2141
Coauthor Index
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last updated on 2024-10-07 22:13 CEST by the dblp team
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