3 citations · 10 across the 9 of their papers we have counts for
6 papers · 1 filter
Open Problem: Optimal Best Arm Identification with Fixed Budget
Chao Qin
Best arm identification or pure exploration problems have received much attention in the COLT community since Bubeck et al. (2009) and Audibert et al. (2010). For any bandit instan…
Contextual Information-Directed Sampling
Botao Hao, Tor Lattimore, Chao Qin
Information-directed sampling (IDS) has recently demonstrated its potential as a data-efficient reinforcement learning algorithm. However, it is still unclear what is the right for…
An Analysis of Ensemble Sampling
Chao Qin, Zheng Wen, Xiuyuan Lu +1
Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In…
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation
Chao Qin, Daniel Russo
We investigate experiments that are designed to select a treatment arm for population deployment. Multi-armed bandit algorithms can enhance efficiency by dynamically allocating mea…
Rate-optimal Bayesian Simple Regret in Best Arm Identification
Junpei Komiyama, Kaito Ariu, Masahiro Kato +1
We consider best arm identification in the multi-armed bandit problem. Assuming certain continuity conditions of the prior, we characterize the rate of the Bayesian simple regret.…
From Predictions to Decisions: The Importance of Joint Predictive Distributions
Zheng Wen, Ian Osband, Chao Qin +5
A fundamental challenge for any intelligent system is prediction: given some inputs, can you predict corresponding outcomes? Most work on supervised learning has focused on produci…