報(bào)告題目：Cost-aware Cascading Bandits
主講嘉賓：Cong Shen 教授 \r
中國(guó)科學(xué)技術(shù)大學(xué)
邀請(qǐng)人：全智教授
時(shí)間：2018年 11月 23日（周 \r
五）上午11：00
地點(diǎn)：深圳大學(xué)南校區(qū)基礎(chǔ)實(shí)驗(yàn)樓北座信息工程學(xué)院N710會(huì)議室
報(bào)告摘要:
We will discuss a \r
cost-aware cascading bandits mode that is motivated by many practical \r
applications. This is a new variant of the multi-armed bandit model but \r
incorporating the random cost of pulling arms and cascading feedback. In each \r
step, the learning agent chooses an ordered list of items and examines them \r
sequentially, until certain stopping condition is satisfied. Our objective is \r
then to maximize the expected net reward in each step, i.e., the reward obtained \r
in each step minus the total cost incurred in examining the items, by deciding \r
the ordered list of items, as well as when to stop examination.
We study both the offline and online settings, depending on whether the state \r
and cost statistics of the items are known beforehand. For the offline setting, \r
we show that the Unit Cost Ranking with Threshold 1 (UCR-T1) policy is optimal. \r
For the online setting, we propose a Cost-aware Cascading Upper Confidence Bound \r
(CC-UCB) algorithm, and show that the cumulative regret scales in O(log T). We \r
also provide a lower bound for all α-consistent policies, which scales in Ω(log \r
T) and matches our upper bound. The performance of the CC-UCB algorithm is \r
evaluated with real-world datasets.Joint work with R. Zhou (University of \r
Science and Technology of China), C. Gan and J. Yang (Pennsylvania State \r
University)
嘉賓簡(jiǎn)介：
Cong Shen received his B.S. and M.S. degrees, in 2002 and 2004 \r
respectively, from the Department of Electronic Engineering, Tsinghua \r
University, China. He obtained the Ph.D. degree from the Electrical Engineering \r
Department, UCLA, in 2009. From 2009 to 2014, He worked for Qualcomm Research in \r
San Diego, CA. In 2015, he joined University of Science and Technology of China \r
(USTC) as Professor in the School of Information Science and Technology. His \r
research interests include machine learning, information theory, and wireless \r
communications. He currently serves as an editor for the IEEE Transactions on \r
Wireless Communications and an editor for the IEEE Wireless Communications
歡迎各位老師和同學(xué)參加。
