Carlos Diuk Wasser Post-doctoral Researcher Department of Psychology and Princeton Neuroscience Institute Princeton University

Contact Me:

• Email: cdiuk princeton edu
• Office Phone:  609-258-1305
• Office:  Green Hall - 3-S-18
• Mailing Address:
  Department of Psychology - Green Hall - Room 3-S-18
  Princeton, NJ 08544

Research Interests:

I am working in the Niv and Botvinick labs at Princeton University, trying to understand how humans use reinforcement learning to learn about their environment and make decisions.

Peer-reviewed Publications:

2011. “A Neural Signature of Hierarchical Reinforcement Learning”, José J.F. Ribas-Fernandes, Alec Solway, Carlos Diuk, Joseph T. McGuire, Andrew G. Barto, Yael Niv and Matthew M. Botvinick. Neuron, Volume 71, Issue 2, 370-379. abstract

2010. “Generalizing Apprenticeship Learning across Hypothesis Classes”, Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman and Carlos Diuk. ICML 2010. pdf

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. pdf

PhD Thesis: "An object-oriented representation for efficient reinforcement learning". pdf

Other publications and talks:

2010. “Hierarchical Reinforcement Learning: An fMRI Study of learning in a two-level gambling task ”, Carlos Diuk, Matthew Botvinick, Andrew Barto and Yael Niv. Society for Neuroscience Meeting 2010 (SfN 2010). pdf

2010. “The emergence of the modern concept of introspection: a quantitative linguistic analysis”, Ivan Raskovsky, Diego Fernández Slezak, Carlos Diuk and Guillermo Cecchi. NAACL Young Investigators Workshop 2010. pdf

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.

Teaching:

Download full CV