TY - GEN
T1 - Distributed learning with infinitely many hypotheses
AU - Nedich, Angelia
AU - Olshevsky, Alex
AU - Uribe, Cesar A.
N1 - Funding Information:
This research is supported partially by the National Science Foundation under grants no. CCF 11-11342 and no. CMMI-1463262 and by the Office of Naval Research under grant no. N00014-12-1-0998
Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/27
Y1 - 2016/12/27
N2 - We consider a distributed learning setup where a network of agents sequentially access realizations of a set of random variables with unknown distributions. The network objective is to find a parametrized distribution that best describes their joint observations in the sense of the Kullback-Leibler divergence. We analyze the case of countably many hypotheses and the case of a continuum of hypotheses. We provide non-asymptotic bounds for the concentration rate of the agents' beliefs around the correct hypothesis in terms of the number of agents, the network parameters, and the learning abilities of the agents. Additionally, we provide a novel motivation for a general set of distributed non-Bayesian update rules as instances of the distributed stochastic mirror descent algorithm.
AB - We consider a distributed learning setup where a network of agents sequentially access realizations of a set of random variables with unknown distributions. The network objective is to find a parametrized distribution that best describes their joint observations in the sense of the Kullback-Leibler divergence. We analyze the case of countably many hypotheses and the case of a continuum of hypotheses. We provide non-asymptotic bounds for the concentration rate of the agents' beliefs around the correct hypothesis in terms of the number of agents, the network parameters, and the learning abilities of the agents. Additionally, we provide a novel motivation for a general set of distributed non-Bayesian update rules as instances of the distributed stochastic mirror descent algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85010817753&partnerID=8YFLogxK
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U2 - 10.1109/CDC.2016.7799242
DO - 10.1109/CDC.2016.7799242
M3 - Conference contribution
AN - SCOPUS:85010817753
T3 - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
SP - 6321
EP - 6326
BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 55th IEEE Conference on Decision and Control, CDC 2016
Y2 - 12 December 2016 through 14 December 2016
ER -