2 citations · 4 across the 3 of their papers we have counts for
9 papers · 1 filter
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
Bing-Jing Hsieh, Ping-Chun Hsieh, Xi Liu
Bayesian optimization (BO) conventionally relies on handcrafted acquisition functions (AFs) to sequentially determine the sample points. However, it has been widely observed in pra…
Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization
Jyun-Li Lin, Wei Hung, Shang-Hsuan Yang +2
Action-constrained reinforcement learning (RL) is a widely-used approach in various real-world applications, such as scheduling in networked systems with resource constraints and c…
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits
Yu-Heng Hung, Ping-Chun Hsieh, Xi Liu +1
Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit tra…
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning
Xi Liu, Li Li, Ping-Chun Hsieh +3
With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experie…
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
Xi Liu, Ping-Chun Hsieh, Anirban Bhattacharya +1
Inspired by the Reward-Biased Maximum Likelihood Estimate method of adaptive control, we propose RBMLE -- a novel family of learning algorithms for stochastic multi-armed bandits (…
Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games
Xi Liu, Muhe Xie, Xidao Wen +4
As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have b…