Machine Learning

  1. Y. H. Kao, B. Van Roy, and X. Yan, ``Directed Regression,'' forthcoming in Advances in Neural Information Processing Systems 22, MIT Press, 2009.

  2. B. Van Roy and X. Yan, ``Manipulation Robustness of Collaborative Filtering Systems,'' submitted for publication.

  3. V. F. Farias, C. C. Moallemi, B. Van Roy, and T. Weissman, ``Universal Reinforcement Learning,'' forthcoming in the IEEE Transactions on Information Theory.

  4. B. Van Roy ``Performance Loss Bounds for Approximate Value Iteration with State Aggregation,'' Mathematics of Operations Research, Vol. 31, No. 2, pp. 234-244, 2006.

  5. C. C. Moallemi and B. Van Roy ``Distributed Optimization in Adaptive Networks,'' Advances in Neural Information Processing Systems 16, MIT Press, 2004. [appendix]

  6. D. S. Choi and B. Van Roy, ``A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal-Difference Learning,'' Discrete Event Dynamic Systems, Vol. 16, No. 2, April 2006.

  7. J. N. Tsitsiklis and B. Van Roy, `` On Average Versus Discounted Reward Temporal-Difference Learning,'' Machine Learning, Vol. 49, No. 2-3, 2002, pp. 179-191.

  8. D. P. de Farias and B. Van Roy, `` On the Existence of Fixed Points for Approximate Value Iteration and Temporal-Difference Learning,'' Journal of Optimization Theory and Applications, Vol. 105, No. 3, June, 2000.

  9. J. N. Tsitsiklis and B. Van Roy, ``Average Cost Temporal-Difference Learning,'' Automatica, Vol. 35, No. 11, November 1999, pp. 1799-1808.

  10. J. N. Tsitsiklis and B. Van Roy, ``An Analysis of Temporal-Difference Learning with Function Approximation,'' IEEE Transactions on Automatic Control, Vol. 42, No. 5, May 1997, pp. 674-690.