253 citations · 276 across the 7 of their papers we have counts for
10 papers
Multi-Fidelity Multi-Armed Bandits Revisited
Xuchuang Wang, Qingyun Wu, Wei Chen +1
We study the multi-fidelity multi-armed bandit (MF-MAB), an extension of the canonical multi-armed bandit (MAB) problem. MF-MAB allows each arm to be pulled with different costs (f…
ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford +2
We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champ…
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
Chuanhao Li, Qingyun Wu, Hongning Wang
Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attrac…
Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
Non-stationary bandits and online clustering of bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios. Though…
Fast Distributed Bandits for Online Recommendation Systems
Kanak Mahadik, Qingyun Wu, Shuai Li +1
Contextual bandit algorithms are commonly used in recommender systems, where content popularity can change rapidly. These algorithms continuously learn latent mappings between user…
Frugal Optimization for Cost-related Hyperparameters
Qingyun Wu, Chi Wang, Silu Huang
The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperpa…