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Carlos Diuk WasserDepartment of Computer
Science |
I'm working with Michael Littman at the Rutgers Laboratory for Real-Life Reinforcement Learning (RL)^3.
I'm currently involved in a couple of projects:
2009. “The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning”, Carlos Diuk, Lihong Li and Bethany Leffler. ICML 2009. pdf / videolecture
2009. “Exploring Compact Reinforcement-Learning Representations with Linear Regression”, Thomas J. Walsh, István Szita, Carlos Diuk, and Michael L. Littman. UAI 2009. pdf
2008. “An Object-Oriented Representation for Efficient Reinforcement Learning”, Carlos Diuk, Andre Cohen and Michael L. Littman. ICML 2008. pdf / videolecture
2008. “Hierarchical Reinforcement Learning”, Carlos Diuk and Michael Littman. Encyclopedia of Artificial Intelligence, IGI Global, July 2008.
2007. “Efficient Structure Learning in Factored-state MDPs”, Alexander L. Strehl, Carlos Diuk and Michael L. Littman. AAAI 2007.pdf
2007. “An adaptive anomaly detector for worm detection ”, John Mark Agosta, Carlos Diuk, Jaideep Chandrashekar and Carl Livadas. Second Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (sysML-07). pdf
2006. “A Hierarchical Approach to Efficient Reinforcement Learning in Deterministic Domains”, Carlos Diuk, Alexander L. Strehl and Michael L. Littman. AAMAS’06. pdf
2003. “Una herramienta computacional para la reconstrucción de genealogías históricas.”, Carlos Diuk. Licenciatura Dissertation. Dept. of Computer Science, Universidad de Buenos Aires.pdf
2002. “Computer tools for reconstructing a genealogy”, Carlos Diuk and Enrique Tándeter. International Journal of History and Computing. Edinburgh University Press.
2006. Invited Speaker at AAMAS Hierarchical Autonomous Agents and Multi-Agent Systems: “A Hierarchical Approach to Efficient Reinforcement Learning”.
2006. “Using Classifiers to Transfer Knowledge ”, Thomas J. Walsh, Carlos Diuk and Michael Littman. Presented at the New York Academy of Science Machine Learning Symposium.
2006. “Efficient exploration and learning of structure in factored-state MDPs ”, Carlos Diuk, Michael L. Littman, Alexander L. Strehl. Presented at NIPS Workshop “Towards a New Reinforcement Learning?”.
2005. “A Hierarchical Approach to Efficient Reinforcement Learning in Factored State Spaces”, Carlos Diuk, Michael L. Littman, and Alexander L. Strehl. Presented at the the 22nd International Conference on Machine Learning (ICML 2005), Workshop on Rich Representations for Reinforcement Learning, Bonn, Germany, 2005.
| Fall 2008 | CS500 - Bayesian Reinforcement Learning |
| Spring 2004 | CS344 - Design and Analysis of Algorithms |
| Fall 2003 | CS344 - Design and Analysis of Algorithms |