no code implementations • 24 Oct 2022 • Xiaoxiao Wang, Nader Bouacida, Xueying Guo, Xin Liu
In this paper, we propose and study opportunistic reinforcement learning - a new variant of reinforcement learning problems where the regret of selecting a suboptimal action varies under an external environmental condition known as the variation factor.
no code implementations • 25 Aug 2021 • Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Veličković
Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike.
no code implementations • 20 Feb 2019 • Xueying Guo, Xiaoxiao Wang, Xin Liu
In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations.
no code implementations • 27 Nov 2018 • Xiaoxiao Wang, Xueying Guo, Jie Chuai, Zhitang Chen, Xin Liu
We evaluate the effectiveness of our algorithm based on a simulator built by real traces.
no code implementations • ICML 2018 • Huasen Wu, Xueying Guo, Xin Liu
When the load/price is low, so is the cost/regret of pulling a suboptimal arm (e. g., trying a suboptimal network configuration).