6 citations · 14 across the 11 of their papers we have counts for
6 papers · 1 filter
Efficient Active Learning with Abstention
Yinglun Zhu, Robert Nowak
The goal of active learning is to achieve the same accuracy achievable by passive learning, while using much fewer labels. Exponential savings in terms of label complexity have bee…
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits
Yinglun Zhu, Julian Katz-Samuels, Robert Nowak
We introduce the model selection problem in pure exploration linear bandits, where the learner needs to adapt to the instance-dependent complexity measure of the smallest hypothesi…
Pure Exploration in Kernel and Neural Bandits
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang +3
We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we prop…
Pareto Optimal Model Selection in Linear Bandits
Yinglun Zhu, Robert Nowak
We study model selection in linear bandits, where the learner must adapt to the dimension (denoted by ) of the smallest hypothesis class containing the true linear model w…
Robust Outlier Arm Identification
Yinglun Zhu, Sumeet Katariya, Robert Nowak
We study the problem of Robust Outlier Arm Identification (ROAI), where the goal is to identify arms whose expected rewards deviate substantially from the majority, by adaptively s…
On Regret with Multiple Best Arms
Yinglun Zhu, Robert Nowak
We study a regret minimization problem with the existence of multiple best/near-optimal arms in the multi-armed bandit setting. We consider the case when the number of arms/actions…