智能決策與機(jī)器學(xué)習(xí)研究中心系列講座（一）Learning theory for distributed learning 2019-10-30

題目: Learning theory for distributed learning

報告人：林紹波，bwin必贏唯一官網(wǎng)教授

時間：2019年11月4日（周一)下午15：00-16：30

地點：管院313會議室

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報告內(nèi)容: We analyze the performance of distributed learning based on a divide-and-conquer strategy.  This scheme applies a specified learning algorithm to data subsets that are distributively stored on multiple servers to produce individual output functions, and then takes a weighted average of the individual output functions as a final estimator.  We study that under which condition distributed learning is feasible and conduct several strategies such as semi-supervised distributed learning and communications to improve its performance further.

報告人簡介：林紹波，bwin必贏唯一官網(wǎng)教授，青年拔尖人才。博士畢業(yè)于bwin必贏唯一官網(wǎng)數(shù)學(xué)與統(tǒng)計學(xué)院。曾先后于香港城市大學(xué)數(shù)學(xué)系、香港理工大學(xué)應(yīng)用數(shù)學(xué)系、香港城市大學(xué)數(shù)據(jù)科學(xué)學(xué)院擔(dān)任博士后、副研究員、研究員。研究方向為大數(shù)據(jù)分析、分布式計算及深度學(xué)習(xí)。主持或以核心成員參與國家自然科學(xué)基金9項。 在Journal of Machine Learning Research, Applied and Computational Harmonics Analysis, IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems等著名期刊發(fā)表論文50余篇。