Publications

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. G. Y. Weintraub, C. L. Benkard, and B. Van Roy, ``Industry Dynamics: Foundations for Models with an Infinite Number of Firms,'' submitted for publication.

4. X. Yan and B. Van Roy, ``Reputation Markets,'' Proceedings of the ACM SIGCOMM 2008 Workshop on Economics of Networks, Systems, and Computation.

5. C. C. Moallemi, B. Park, and B. Van Roy, ``Strategic Execution in the Presence of an Uninformed Arbitrageur,'' submitted for publication.

6. B. Van Roy, ``On Regression-Based Stopping Times,'' forthcoming in Discrete Event Dynamic Systems.

7. C. C. Moallemi and B. Van Roy, ``Convergence of the Min-Sum Algorithm for Convex Optimization,'' forthcoming in the IEEE Transactions on Information Theory.

8. J. Han and B. Van Roy, ``Control of Diffusions via Linear Programming,'' to appear in a volume on stochastic programming in honor of George Dantzig, edited by Gerd Infanger.

9. V. F. Farias and B. Van Roy, ``An Approximate Dynamic Programming Approach to Network Revenue Management,'' submitted for publication.

10. C. C. Moallemi and B. Van Roy, ``A Message-Passing Paradigm for Resource Allocation,'' submitted for publication.

11. C. C. Moallemi, S. Kumar, and B. Van Roy, ``Approximate and Data-Driven Dynamic Programming for Queueing Networks,'' submitted for publication.

12. H. Permuter, P. Cuff, B. Van Roy, and T. Weissman, ``Capacity of the Trapdoor Channel with Feedback,'' IEEE Transactions on Information Theory, Vol. 54, No. 7, pp. 3150-3165, 2008.

13. V. F. Farias and B. Van Roy, ``Dynamic Pricing with a Prior on Market Response,'' forthcoming in Operations Research.

14. B. Van Roy, ``A Short Proof of Optimality for the MIN Cache Replacement Algorithm,'' Information Processing Letters, Vol. 102, No. 2, pp. 72-73, 2007.

15. C. C. Moallemi and B. Van Roy, ``Convergence of Min-Sum Message Passing for Quadratic Optimization,'' IEEE Transactions on Information Theory, Vol. 55, No. 5, pp. 2413-2423, 2009.

16. R. Johari, G. Y. Weintraub, and B. Van Roy, ``Investment and Market Structure in Industries with Congestion,'' forthcoming in Operations Research.

17. G. Y. Weintraub, C. L. Benkard, and B. Van Roy, ``Computational Methods for Oblivious Equilibrium,'' forthcoming in Operations Research. [Matlab code (updated July 2008)]

18. G. Y. Weintraub, C. L. Benkard, and B. Van Roy, ``Markov Perfect Industry Dynamics with Many Firms,'' Econometrica, Vol. 76, No. 6, 2008, pp. 1375-1411. [Technical Appendix]
    Preliminary version:
    • G. Y. Weintraub, C. L. Benkard, and B. Van Roy, ``Oblivious Equilibrium: A Mean Field Approximation for Large Scale Dynamic Games,'' Advances in Neural Information Processing Systems 18, MIT Press, 2006.

19. C. C. Moallemi and B. Van Roy, ``Consensus Propagation,'' IEEE Transactions on Information Theory, Vol. 52, No. 11, pp. 4753-4766, 2006.
    Preliminary version:
    • C. C. Moallemi and B. Van Roy, ``Consensus Propagation,'' Advances in Neural Information Processing Systems 18, MIT Press, 2006.

20. V. F. Farias, C. C. Moallemi, B. Van Roy, and T. Weissman, ``Universal Reinforcement Learning,'' forthcoming in the IEEE Transactions on Information Theory.
    Preliminary version:
    • V. F. Farias, C. C. Moallemi, B. Van Roy, and T. Weissman, ``A Universal Scheme for Learning,'' Proceedings of the IEEE International Symposium on Information Theory, Adelaide, Australia, September 2005.

21. H. Zhang, A. Goel, R. Govindan, K. Mason, and B. Van Roy, ``Improving Eigenvector-Based Reputation Systems Against Collusion,'' Workshop on Algorithms and Models for the Web Graph, October 2004.

22. P. Rusmevichientong, J. A. Salisbury, L. T. Truss, B. Van Roy, and P. W. Glynn, ``Opportunities and Challenges in Using Online Preference Data for Vehicle Pricing: A Case Study at General Motors,'' Journal of Revenue and Pricing Management, Vol. 5, No. 1, pp. 45-61, 2006.

23. D. P. de Farias and B. Van Roy, ``A Cost-Shaping Linear Program for Average-Cost Approximate Dynamic Programming with Performance Guarantees,'' Mathematics of Operations Research, Vol. 31, No. 3, pp. 597-620, 2006.
    Preliminary versions:
    • D. P. de Farias and B. Van Roy, ``A Linear Program for Bellman Error Minimization with Performance Guarantees,'' Advances in Neural Information Processing Systems 17, MIT Press, 2005.
    • D. P. de Farias and B. Van Roy, ``Approximate Linear Programming for Average-Cost Dynamic Programming,'' Advances in Neural Information Processing Systems 15, MIT Press, 2003.

24. V. F. Farias and B. Van Roy ``Approximation Algorithms for Dynamic Resource Allocation,'' Operations Research Letters, Vol. 34, No. 2, March 2006, pp. 180-190.

25. X. Yan, P. Diaconis, P. Rusmevichientong, and B. Van Roy, ``Solitaire: Man Versus Machine,'' Advances in Neural Information Processing Systems 17, MIT Press, 2005.

26. R. Cogill, M. Rotkowitz, B. Van Roy, S. Lall, ``An Approximate Dynamic Programming Approach to Decentralized Control of Stochastic Systems,'' Lecture Notes in Control and Information Sciences, Springer, Berlin, 2006, Vol. 329, pp. 243-256.
    Preliminary version:
    • R. Cogill, M. Rotkowitz, B. Van Roy, S. Lall, ``An Approximate Dynamic Programming Approach to Decentralized Control of Stochastic Systems,'' Proceedings of the Allerton Conference on Communication, Control, and Computing, 2004, pp. 1040-1049.

27. 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.
    Preliminary version:
    • B. Van Roy, ``TD(0) Leads to Better Policies than Approximate Value Iteration,'' Advances in Neural Information Processing Systems 18, MIT Press, 2006.

28. V. F. Farias and B. Van Roy, ``Tetris: A Study of Randomized Constraint Sampling,'' in Probabilistic and Randomized Methods for Design Under Uncertainty, G. Calafiore and F. Dabbene, eds., Springer-Verlag, 2006.

29. W. B. Powell and B. Van Roy, ``Approximate Dynamic Programming for High-Dimensional Dynamic Resource Allocation Problems,'' in Handbook of Learning and Approximate Dynamic Programming, edited by J. Si, A. G. Barto, W. B. Powell, and D. Wunsch, Wiley-IEEE Press, Hoboken, NJ, 2004, pp. 261-279.

30. C. C. Moallemi and B. Van Roy ``Distributed Optimization in Adaptive Networks,'' Advances in Neural Information Processing Systems 16, MIT Press, 2004. [appendix]
    Preliminary version:
    • C. C. Moallemi and B. Van Roy, ``Decentralized Protocols for Optimization of Sensor Networks,'' Proceedings of Allerton 2003.

31. P. Rusmevichientong, B. Van Roy, and P. W. Glynn, ``A Non-Parametric Approach to Multi-Product Pricing,'' Operations Research, Vol. 54, No. 1, 2006, pp. 82-98.

32. B. Van Roy, ``Book Review: Self-Learning Control of Finite Markov Chains, by A. S. Poznyak, K. Najim, and E. Gomez-Ramirez,'' Automatica, Volume 39, Issue 2, February 2003, pp. 373-376.

33. 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.
    Preliminary version:
    • D. S. Choi and B. Van Roy, ``A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal-Difference Learning,'' Proceedings of the International Conference on Machine Learning, 2001.

34. N. Agarwal, J. Basch, P. Beckmann, P. Bharti, S. Bloebaum, S. Casadei, A. Chou, P. Enge, W. Fong, N. Hathi, W. Mann, A. Sahai, J. Stone, J. Tsitsiklis, and B. Van Roy, ``Algorithms for GPS Operation Indoors and Downtown,'' GPS Solutions, Vol. 6, No. 3, December, 2002, pp. 149-160.

35. P. Rusmevichientong and B. Van Roy, ``A Tractable POMDP for a Class of Sequencing Problems,'' Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2001.

36. D. P. de Farias and B. Van Roy, `` On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming,'' Mathematics of Operations Research, Vol. 29, No. 3, August 2004, pp. 462-478.

37. D. P. de Farias and B. Van Roy, ``The Linear Programming Approach to Approximate Dynamic Programming,'' Operations Research, Vol. 51, No. 6, November-December 2003, pp. 850-865.
    Preliminary version:
    • D.P. de Farias and B. Van Roy, ``Approximate Dynamic Programming via Linear Programming,'' Advances in Neural Information Processing Systems 14, MIT Press, 2002.

38. P. Rusmevichientong and B. Van Roy, `` Decentralized Decision-Making in a Large Team with Local Information,'' Games and Economic Behavior , Vol. 43, No. 2, 2003, pp. 266-295.

39. N. O. Keohane, B. Van Roy, and R. J. Zeckhauser, ``Managing the Quality of a Resource with Stock and Flow Controls,'' Journal of Public Economics, Vol. 91, 2007, pp. 541-569.
    Preliminary version:
    • N. O. Keohane, B. Van Roy, and R. J. Zeckhauser, ``The Optimal Harvesting of Environmental Bads,'' Proceedings of the IEEE Conference on Decision and Control, 2000.

40. B. Van Roy, `` Neuro-Dynamic Programming: Overview and Recent Trends,'' in Handbook of Markov Decision Processes: Methods and Applications, edited by E. Feinberg and A. Shwartz, Kluwer, 2001.

41. 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.

42. J. N. Tsitsiklis and B. Van Roy, ``Regression Methods for Pricing Complex American-Style Options,'' IEEE Transactions on Neural Networks, Vol. 12, No. 4 (special issue on computational finance), July 2001, pp. 694-703.

43. P. Rusmevichientong and B. Van Roy, `` An Analysis of Belief Propagation on the Turbo Decoding Graph with Gaussian Densities,'' IEEE Transactions on Information Theory, Vol. 47, No. 2, pp. 745-765, 2001.
    Preliminary version:
    • P. Rusmevichientong and B. Van Roy, ``An Analysis of Turbo Decoding with Gaussian Priors,'' Advances in Neural Information Processing Systems 12, MIT Press, 2000.

44. 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.
    Preliminary versions:
    • D. P. de Farias and B. Van Roy, ``Approximate Value Iteration with Randomized Policies,'' Proceedings of the IEEE Conference on Decision and Control, 2000.
    • D. P. de Farias and B. Van Roy, ``Approximate Value Iteration and Temporal-Difference Learning,'' Proceedings of the IEEE Symposium 2000 on Adaptive Systems for Signal Processing, Communications and Control, 2000.
    • D. P. de Farias and B. Van Roy, ``Fixed Points for Approximate Value Iteration and Temporal-Difference Learning,'' Proceedings of the International Conference on Machine Learning, 2000.

45. J. N. Tsitsiklis and B. Van Roy, ``Average Cost Temporal-Difference Learning,'' Automatica,Vol. 35, No. 11, November 1999, pp. 1799-1808.
    Preliminary version:
    • J. N. Tsitsiklis and B. Van Roy, ``Average Cost Temporal-Difference Learning,'' Proceedings of the IEEE Conference on Decision and Control, 1997.

46. B. Van Roy, ``Temporal-Difference Learning and Applications in Finance,'' Computational Finance (Proceedings of the Sixth International Conference on Computational Finance, Leonard N. Stern School of Business, January 6-8, 1999). Edited by Y. S. Abu-Mostafa, B. LeBaron, A. W. Lo, and A. S. Weigend. Cambridge, MA: MIT Press, 1999.

47. J. N. Tsitsiklis and B. Van Roy, ``Optimal Stopping of Markov Processes: Hilbert Space Theory, Approximation Algorithms, and an Application to Pricing High-Dimensional Financial Derivatives,'' IEEE Transactions on Automatic Control, Vol. 44, No. 10, October 1999, pp. 1840-1851.
    Preliminary versions:
    • J. N. Tsitsiklis and B. Van Roy, ``Overview of Neuro-Dynamic Programming and a Case Study in Optimal Stopping,'' Proceedings of the IEEE Conference on Decision and Control, 1997.
    • J. N. Tsitsiklis and B. Van Roy, ``Approximate Solutions to Optimal Stopping Problems,'' Advances in Neural Information Processing Systems 9, MIT Press, 1997.

48. 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.
    Preliminary version:
    • J. N. Tsitsiklis and B. Van Roy, ``Analysis of Temporal-Difference Learning with Function Approximation,'' Advances in Neural Information Processing Systems 9, MIT Press, 1997.

49. J. N. Tsitsiklis and B. Van Roy, ``Feature-Based Methods for Large Scale Dynamic Programming,'' Machine Learning, Vol. 22, 1996, pp. 59-94.

50. B. Van Roy, D. P. Bertsekas, Y. Lee, and J. N. Tsitsiklis, ``A Neuro-Dynamic Programming Approach to Retailer Inventory Management,'' Proceedings of the IEEE Conference on Decision and Control, 1997. (full length version)

51. B. Van Roy and J. N. Tsitsiklis, `` Stable Linear Approximations to Dynamic Programming for Stochastic Control Problems with Local Transitions,'' Advances in Neural Information Processing Systems 8, MIT Press, 1996.

52. R. Kennedy, Y. Lee, B. Van Roy, C. Reed, and R. Lippman, Solving Data Mining Problems Through Pattern Recognition, Prentice-Hall, 1997.
    Preliminary version:
    • R. Kennedy, Y. Lee, C. Reed, and B. Van Roy, Solving Pattern Recognition Problems, Unica, 1995.

Theses

1. B. Van Roy, ``Learning and Value Function Approximation in Complex Decision Processes,'' PhD Thesis, Massachusetts Institute of Technology, May 1998.

2. B. Van Roy, ``Feature-Based Methods for Large Scale Dynamic Programming,'' Master's Thesis, Massachusetts Institute of Technology, January 1995.

3. B. Van Roy, ``Differential Cost Functions for Training Neural Network Pattern Classifiers,'' Bachelor's Thesis, Massachusetts Institute of Technology, May 1993.