The following is a list of example papers for student presentations. If you
want to present one of these papers, please let me know as soon as possible.
If you have a different paper you would like to present, let me know what the
Presentations will be 12 minutes long (plus 3 minutes for questions). The main
goal of the presentations should be to present and discuss different
applications and techniques in uncertainty reasoning with a particular focus
on how the techniques are applied in differrent contexts. Your presentation should include a basic description of the paper and the techniques used as well as your assessment and analysis thereof. (Keep in mind that your
presentations should be aimed at your classmates and not assume any
prerequisites beyond this course and the prerequisites to this course).
- Kohl, N., & Stone, P. (2004, May). Policy gradient reinforcement learning for fast quadrupedal locomotion. In Robotics and Automation, 2004. Proceedings. ICRA'04. 2004 IEEE International Conference on (Vol. 3, pp. 2619-2624). IEEE.
- Peters, J., & Schaal, S. (2008). Reinforcement learning of motor skills with policy gradients. Neural networks, 21(4), 682-697.
- Michels, J., Saxena, A., & Ng, A. Y. (2005, August). High speed obstacle avoidance using monocular vision and reinforcement learning. In Proceedings of the 22nd international conference on Machine learning (pp. 593-600). ACM.
- Galstyan, A., Czajkowski, K., & Lerman, K. (2004, July). Resource allocation in the grid using reinforcement learning. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3 (pp. 1314-1315). IEEE Computer Society.
- Mahmood, T., & Ricci, F. (2007, August). Learning and adaptivity in interactive recommender systems. In Proceedings of the ninth international conference on Electronic commerce (pp. 75-84). ACM.
- Seymour, B., O'Doherty, J. P., Dayan, P., Koltzenburg, M., Jones, A. K., Dolan, R. J., ... & Frackowiak, R. S. (2004). Temporal difference models describe higher-order learning in humans. Nature, 429(6992), 664-667.
Presentations will be on the following dates. A sign up, please send email to the instructor. In order to keep papers grouped by topic area, final time slot
assignments can not be guaranteed to match your preferences.
- Rasool Fakoor: Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M. (2013). "Playing Atari with Deep Reinforcement Learning". arXiv preprint arXiv:1312.5602.
- Brian Cook: Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3), 58-68.
- Konstantinos Tsiakas: P.-H. Su, Y.-B. Wang, T.-H. Yu, and L.-S. Lee, "A dialogue game framework with personalized training using reinforcement learning for computer-assisted language learning," in ICASSP, 2013.
- Sourabh Bose: Peng, J., & Bhanu, B. (1998). Closed-loop object recognition using reinforcement learning. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(2), 139-154.
- Oluwatosin Oluwadare: Mahadevan, S., & Connell, J. (1992). Automatic programming of behavior-based robots using reinforcement learning. Artificial intelligence, 55(2), 311-365.
- Shirin Shirvani: Grefenstette, J. J., Moriarty, D. E., & Schultz, A. C. (2011). "Evolutionary algorithms for reinforcement learning". arXiv preprint arXiv:1106.0221.
- Mostafa Parchami: Sahba, F., & Tizhoosh, H. R. (2003, May). "Filter fusion for image enhancement using reinforcement learning". In Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on (Vol. 2, pp. 847-850). IEEE.
- Ramakrishna Aduri: McCallum, A. (2011). "Selecting Actions for Resource-bounded Information Extraction using Reinforcement Learning".
- Mohsen Imani: Peshkin, L., & Savova, V. (2002). "Reinforcement learning for adaptive routing". In Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference on (Vol. 2, pp. 1825-1830). IEEE.
- Mohammadhani Fouladgar: Boyan, J., Freitag, D., & Joachims, T. (1996, August). "A machine learning architecture for optimizing web search engines". In AAAI Workshop on Internet Based Information Systems (pp. 1-8).
- Rommel Alonzo: Yin, P. Y., Bhanu, B., Chang, K. C., & Dong, A. (2005). "Integrating relevance feedback techniques for image retrieval using reinforcement learning". Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10), 1536-1551.