Justin A. Boyan

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Justin A. Boyan [1] Justin Andrew Boyan,

an American computer scientist and product manager at Google. He holds a B.Sc. in mathematics from University of Chicago in 1991, a Master of Philosophy in computer speech and language processing from University of Cambridge in 1992 [2], and a Ph.D. in computer science from Carnegie Mellon University in 1998, where his advisors include Scott Fahlman and Andrew W. Moore [3] [4]. Until 2000, he was research scientist at NASA Ames Research Center with assignment as visiting scientist to MIT AI Lab. His research interests include artificial intelligence, statistical machine learning, information extraction, and optimization. In the early 90s, he wrote software combining temporal difference learning and modular neural networks in order to train an expert-level computer Backgammon player from scratch. His program Maestro placed second at the 4th Computer Olympiad, London 1992 [5].

Justin A. Boyan (1992). Modular Neural Networks for Learning Context-Dependent Game Strategies. Master’s thesis, University of Cambridge, pdf
Justin A. Boyan, Michael L. Littman (1993). Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach. NIPS 1993, pdf
Justin A. Boyan, Andrew W. Moore (1994). Generalization in Reinforcement Learning: Safely Approximating the Value Function. NIPS 1994, pdf
Justin A. Boyan, Andrew W. Moore (1996). Learning Evaluation Functions for Large Acyclic Domains. ICML 1996, pdf
Justin A. Boyan (1996). A Reinforcement Learning Framework for Combinatorial Optimization. AAAI/IAAI-96, Vol. 2, pdf
Justin A. Boyan, Andrew W. Moore (1998). Learning Evaluation Functions for Global Optimization and Boolean Satisfiability. AAAAI/IAAI 1998, pdf
Justin A. Boyan (1998). Least-Squares Temporal Difference Learning. Carnegie Mellon University, CMU-CS-98-152, pdf [8]
Justin A. Boyan (1998). Learning Evaluation Functions for Global Optimization. Ph.D. thesis, Carnegie Mellon University, advisors Scott Fahlman and Andrew W. Moore, zipped ps

Justin A. Boyan (2002). Technical Update: Least-Squares Temporal Difference Learning. Machine Learning, Vol. 49, pdf
Amy Greenwald, Justin A. Boyan (2004, 2012). Bidding under Uncertainty: Theory and Experiments. UAI 2004, arXiv:1207.4108

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