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Biostatistics and Statistics Fall 2019 Seminar: Communication-Efficient Accurate Statistical Estimation

Biostatistics and Statistics Fall 2019 Seminar: Communication-Efficient Accurate Statistical Estimation

Date 
Wednesday, November 20, 2019 - 3:30pm to 4:30pm
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Location 
CHS 33-105A Los Angeles , CA
California US
Featuring 
Jianqing Fan
Event Contact 

wkwong@ucla.edu
310-206-9622

Jianqing Fan
Professor of Statistics
Princeton University

When the data are stored in a distributed manner, direct application of traditional statistical inference procedures is often prohibitive due to communication cost and privacy concerns. This paper develops and investigates two Communication-Efficient Accurate Statistical Estimators (CEASE), implemented through iterative algorithms for distributed optimization. In each iteration, node machines carry out computation in parallel and communicates with the central processor, which then broadcasts aggregated gradient vector to node machines for new updates. The algorithms adapt to the similarity among loss functions on node machines, and converge rapidly when each node machine has large enough sample size. Moreover, they do not require good initialization and enjoy linear converge guarantees under general conditions. The contraction rate of optimization errors is derived explicitly, with dependence on the local sample size unveiled. In addition, the improved statistical accuracy per iteration is derived. By regarding the proposed method as a multi-step statistical estimator, we show that statistical efficiency can be achieved in finite steps in typical statistical applications. In addition, we give the conditions under which one-step CEASE estimator is statistically efficient. Extensive numerical experiments on both synthetic and real data validate the theoretical results and demonstrate the superior performance of our algorithms. (Joint work with Yongyi Guo and Kaizheng Wang)

Refreshments served at 3:00pm in CHS 51-254
Hosted by Department of Biostatistics and Statistics at UCLA