Brian Swenson
Ph.D. Students
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[1] Brian Swenson, S. Kar, João Xavier, D. Leslie, "Robustness properties in fictitious-play-type algorithms", SIAM Journal on Control and Optimisation, Vol. 55, No. 5, pp. 3295-3318, October, 2017
[2] Brian Swenson, S. Kar, João Xavier, "Single sample fictitious play", IEEE Transactions on Automatic Control, Vol. 62, No. 11, pp. 6026-6031, May, 2017
[3] Brian Swenson, S. Kar, João Xavier, "Empirical Centroid Fictitious Play: An Approach for Distributed Learning in Multi-Agent Games", IEEE Transactions on Signal Processing, Vol. 63, No. 15, August, 2015 - PDF
[1] Brian Swenson, S. Kar, João Xavier, "Computationally efficient learning in large-scale games: sampled fictitious play revisited", Proc. of ASILOMAR 2016 - 50th Annual Asilomar Conference on Signals, Systems, and Computers, pp. 1212-1215, Pacific Grove, CA, USA, 2017
[2] Brian Swenson, S. Kar, João Xavier, "On robustness properties in empirical centroid fictitious play", Proc. of CDC 2015 - 54th IEEE Conference on Decision and Control, pp. 3324-3330, Osaka, Japan, 2015
[3] Brian Swenson, S. Kar, João Xavier, "On asynchronous implementations of fictitious play for distributed learning", Proc. of ASILOMAR 2015 - 49th Annual Asilomar Conference on Signals, Systems, and Computers, pp. 1119-1124, Pacific Grove, CA, USA, 2015
[4] Brian Swenson, S. Kar, João Xavier, "A computationally efficient implementation of fictitious play in a distributed setting", Proc. of EUPSICO 2015 - 23rd European Signal Processing Conference, pp. 1043-1047, Nice, France, 2015
[5] Brian Swenson, João Xavier, "Strong Convergence to Mixed Equilibria in Fictitious Play", (CISS) 48th annual conference on information sciences and systems, Princeton University, March, 2014 - PDF
[1] Brian Swenson, "Myopic Best-Response Learning in Large-Scale Games", Ph.D. Thesis, within the IST-CMU Joint Doctoral Initiative, April, 2017