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Research is funded by the DARPA ITMANET project, Motorola, as well as the National Science Foundation. See also the Information Theory page. Control Techniques for Complex Networks - Chapter 11 is devoted to simulation and machine learning
Q-learning and Pontryagin's Minimum Principle, Prashant Mehta and Sean Meyn. Approximate Dynamic Programming using Fluid and Diffusion Approximations with Applications to Power Management, Wei Chen, Dayu Huang, Ankur Kulkarni, Jayakrishnan Unnikrishnan, Quanyan Zhu, Prashant Mehta, Sean Meyn, and Adam Wierman. Control Variates as Screening Functions, Sofia Kyriazopoulou-Panagiotopoulou, Ioannis Kontoyiannis, and Sean Meyn. VALUETOOLS 2008 - Third International Conference on Performance Evaluation Methodologies and Tools. October 20-24, 2008, Athens, Greece. Most likely paths to error when estimating the mean of a reflected random walk. K. R. Duffy and S. P. Meyn. Submitted for publication, June 2009. Shannon meets Bellman: Feature based Markovian models for detection and optimization, George Mathew and Sean Meyn. Waveform Relaxation and Graph Decomposition, George Mathew, Sean Meyn, Andrzej Banaszuk. 18th International symposium on Mathematical Theory of Networks and Systems (MTNS2008) Virginia Tech, Blacksburg, Virginia, USA July 28-August 1, 2008. Least favorable distributions for robust quickest change detection, J. Unnikrishnan, V. Venugopal, and S. Meyn. International Symposium on Information Theory (ISIT), June 2009. Exponential bounds and stopping rules for MCMC and general Markov chains, I. Kontoyiannis , L. A. Lastras-Montaño , S. P. Meyn. Proceedings of the 1st international conference on Performance evaluation methodolgies and tools, October 11-13, 2006, Pisa, Italy. The ODE methods for Markov chain stability with applications to MCMC, G. Fort, S. Meyn, E. Moulines, and P. Priouret. Computable Exponential Bounds for Screened Estimation and Simulation, I. Kontoyiannis and S.P. Meyn. Finding the Best Mismatched Detector for Channel Coding and Hypothesis Testing, E. Abbe, M. Medard, S. P. Meyn, and L. Zheng. Worst-Case Large-Deviations Asymptotics with Application to Queueing and Information Theory, C. Pandit, and S.P. Meyn. Stochastic Processes and Applications 116(5) pp. 724-756, 2006. Extremal Distributions in Information Theory and Hypothesis Testing, C. Pandit, J. Huang, S. Meyn, V. Veeravalli. Proceedings of the IEEE Information Theory Workshop, San Antonio, Texas, October 24-29, 2004. Randomized Algorithms for Semi-Infinite Programming Problems, V. Tadic, S.P. Meyn and R. Tempo. Probabilistic and Randomized Methods for Design under Uncertainty, Springer Verlag, 2005. V. Tadic and S.P. Meyn, Asymptotic Properties of Two Time-Scale Stochastic Approximation Algorithms with Constant Step Sizes, Proceedings of the 2003 American Control Conference June 4 to 6, 2003. J. Huang, Kontoyiannis, I. and S.P. Meyn, The O.D.E. Method and Spectral Theory of Markov Operators (also available in pdf format), Proceedings of the Second Kansas Workshop on Stochastic Theory - Adaptive Control, 2001 V. Borkar and S.P. Meyn, The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning, SIAM J. Control, Vol. 38, no.2, 2000, pp. 447-69. S.R. Rayadurgam and S.P. Meyn, Bounds on Achievable Performance in Adaptive Control, IEEE Transactions on Automatic Control, Vol 44, No 4, pp. 670--682, 1999. L.J. Brown, S.P. Meyn, and R. Weber, Adaptive Dead-Time Compensation with Applications, IEEE J. Control Systems Technology, vol 6, pp. 335-349, 1998. S.P. Meyn and L. J. Brown, Model Reference Adaptive Control of Time Varying and Stochastic Systems, IEEE Transactions on Automatic Control, Vol. 38, pp. 1738--1753, 1993
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