Haitham Bou Ammar
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics 22
- Evolutionary Algorithms and Applications 5
- Domain Adaptation and Few-Shot Learning 5
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- Human Pose and Action Recognition 4
- Robotic Path Planning Algorithms 3
- Control and Systems Engineering top 10%
- Robot Manipulation and Learning 3
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- Adaptive Dynamic Programming Control 9
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- Advanced Bandit Algorithms Research 4
Haitham Bou Ammar
39 papers receiving 554 citations
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 332
- Computer Vision and Pattern Recognition 152
- Control and Systems Engineering 126
- Computational Theory and Mathematics 78
- Management Science and Operations Research 43
Countries citing papers authored by Haitham Bou Ammar
This map shows the geographic impact of Haitham Bou Ammar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Haitham Bou Ammar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haitham Bou Ammar more than expected).
Fields of papers citing papers by Haitham Bou Ammar
This network shows the impact of papers produced by Haitham Bou Ammar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Haitham Bou Ammar. The network helps show where Haitham Bou Ammar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Haitham Bou Ammar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2023 | 6 | |
| 3 | 2023 | 26 | |
| 4 | 2021 | 4 | |
| 5 | Robot Reinforcement Learning on the Constraint Manifold | 2021 | 5 |
| 6 | 2020 | 0 | |
| 7 | Distributed Multitask Reinforcement Learning with Quadratic Convergence | 2018 | 5 |
| 8 | Theoretically-grounded policy advice from multiple teachers in reinforcement learning settings with applications to negative transfer | 2016 | 2 |
| 9 | Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning | 2015 | 30 |
| 10 | 2014 | 1 | |
| 11 | An automated measure of MDP similarity for transfer in reinforcement learning | 2014 | 29 |
| 12 | Online Multi-Task Learning for Policy Gradient Methods | 2014 | 74 |
| 13 | 2014 | 4 | |
| 14 | 2014 | 1 | |
| 15 | Conditional restricted Boltzmann machines for negotiations in highly competitive and complex domains | 2013 | 7 |
| 16 | Dynamic Object Recognition using Sparse Coded Three-way Conditional Restricted Boltzmann Machines | 2013 | 1 |
| 17 | 2012 | 32 | |
| 18 | A Nonparametric Evaluation of SysML-based Mechatronic Conceptual Design | 2012 | 4 |
| 19 | Teaching Reinforcement Learning using a Physical Robot | 2012 | 5 |
| 20 | Common Subspace Transfer for Reinforcement Learning Tasks | 2011 | 3 |
About Haitham Bou Ammar
Haitham Bou Ammar is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research, having authored 44 papers that have together received 589 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (22 papers), Adaptive Dynamic Programming Control (9 papers), Evolutionary Algorithms and Applications (5 papers), Domain Adaptation and Few-Shot Learning (5 papers), Human Pose and Action Recognition (4 papers), Advanced Bandit Algorithms Research (4 papers), Robot Manipulation and Learning (3 papers) and Robotic Path Planning Algorithms (3 papers). The work is most often cited by research in Artificial Intelligence (332 citations), Computer Vision and Pattern Recognition (152 citations) and Control and Systems Engineering (126 citations). Haitham Bou Ammar has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Eric Eaton, Matthew E. Taylor, Holger Voos, Paul Ruvolo, Karl Tuyls, Gerhard Weiß, Wolfgang Ertel, Decebal Constantin Mocanu, Kurt Driessens and Antonio Liotta. Their work appears in journals such as Pattern Recognition, Neural Computation and Machine Learning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.