2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Infinite Action Contextual Bandits with Reusable Data Exhaust
Mark Rucker, Yinglun Zhu, Paul Mineiro
For infinite action contextual bandits, smoothed regret and reduction to regression results in state-of-the-art online performance with computational cost independent of the action…
cs.LG2022★ 2 cited
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces
Yinglun Zhu, Paul Mineiro
Designing efficient general-purpose contextual bandit algorithms that work with large -- or even continuous -- action spaces would facilitate application to important scenarios suc…
cs.LG2022
Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan J. Foster, John Langford +1
A central problem in sequential decision making is to develop algorithms that are practical and computationally efficient, yet support the use of flexible, general-purpose models.…