6 citations · 14 across the 11 of their papers we have counts for
11 papers
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…
Active Learning with Neural Networks: Insights from Nonparametric Statistics
Yinglun Zhu, Robert Nowak
Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly r…
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…
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.…
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…
ReabsNet: Detecting and Revising Adversarial Examples
Jiefeng Chen, Zihang Meng, Changtian Sun +2
Though deep neural network has hit a huge success in recent studies and applica- tions, it still remains vulnerable to adversarial perturbations which are imperceptible to humans.…