62 citations · 286 across the 40 of their papers we have counts for
6 papers · 1 filter
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuan Zhou, Hongseok Yang, Yee Whye Teh +1
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing in…
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
Yingkai Li, Yining Wang, Xi Chen +1
Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and…
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits
Yingkai Li, Yining Wang, Yuan Zhou
We study the linear contextual bandit problem with finite action sets. When the problem dimension is , the time horizon is , and there are candidate actions…
On Exploration, Exploitation and Learning in Adaptive Importance Sampling
Xiaoyu Lu, Tom Rainforth, Yuan Zhou +2
We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation. Bor…
Dynamic Assortment Selection under the Nested Logit Models
Xi Chen, Chao Shi, Yining Wang +1
We study a stylized dynamic assortment planning problem during a selling season of finite length . At each time period, the seller offers an arriving customer an assortment of s…
An Optimal Policy for Dynamic Assortment Planning Under Uncapacitated Multinomial Logit Models
Xi Chen, Yining Wang, Yuan Zhou
We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and customer makes the purchase among…