1 citations · 1 across the 5 of their papers we have counts for
7 papers
Empirical Bayesian Multi-Bandit Learning
Xia Jiang, Rong J. B. Zhu
Multi-task learning in contextual bandits has attracted significant research interest due to its potential to enhance decision-making across multiple related tasks by leveraging sh…
High dimensional Bayesian Optimization via Condensing-Expansion Projection
Jiaming Lu, Rong J. B. Zhu
In high-dimensional settings, Bayesian optimization (BO) can be expensive and infeasible. The random embedding Bayesian optimization algorithm is commonly used to address high-dime…
UCB Exploration for Fixed-Budget Bayesian Best Arm Identification
Rong J. B. Zhu, Yanqi Qiu
We study best-arm identification (BAI) in the fixed-budget setting. Adaptive allocations based on upper confidence bounds (UCBs), such as UCBE, are known to work well in BAI. Howev…
Robust Contextual Linear Bandits
Rong Zhu, Branislav Kveton
Model misspecification is a major consideration in applications of statistical methods and machine learning. However, it is often neglected in contextual bandits. This paper studie…
Gradient Descent Temporal Difference-difference Learning
Rong J. B. Zhu, James M. Murray
Off-policy algorithms, in which a behavior policy differs from the target policy and is used to gain experience for learning, have proven to be of great practical value in reinforc…
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks
Rong Zhu, Mattia Rigotti
Designing efficient exploration is central to Reinforcement Learning due to the fundamental problem posed by the exploration-exploitation dilemma. Bayesian exploration strategies l…