13 citations · 18 across the 5 of their papers we have counts for
13 papers
Gaussian Imagination in Bandit Learning
Yueyang Liu, Adithya M. Devraj, Benjamin Van Roy +1
Assuming distributions are Gaussian often facilitates computations that are otherwise intractable. We study the performance of an agent that attains a bounded information ratio wit…
Learning and Information in Stochastic Networks and Queues
Neil Walton, Kuang Xu
We review the role of information and learning in the stability and optimization of queueing systems. In recent years, techniques from supervised learning, bandit learning and rein…
Hierarchical Causal Bandit
Ruiyang Song, Stefano Rini, Kuang Xu
Causal bandit is a nascent learning model where an agent sequentially experiments in a causal network of variables, in order to identify the reward-maximizing intervention. Despite…
A Bit Better? Quantifying Information for Bandit Learning
Adithya M. Devraj, Benjamin Van Roy, Kuang Xu
The information ratio offers an approach to assessing the efficacy with which an agent balances between exploration and exploitation. Originally, this was defined to be the ratio b…
Learner-Private Convex Optimization
Jiaming Xu, Kuang Xu, Dana Yang
Convex optimization with feedback is a framework where a learner relies on iterative queries and feedback to arrive at the minimizer of a convex function. It has gained considerabl…
Query Complexity of Bayesian Private Learning
Kuang Xu
We study the query complexity of Bayesian Private Learning: a learner wishes to locate a random target within an interval by submitting queries, in the presence of an adversary who…