102 citations · 115 across the 10 of their papers we have counts for
10 papers
Lipschitz Bandits with Stochastic Delayed Feedback
Zhongxuan Liu, Yue Kang, Thomas C. M. Lee
The Lipschitz bandit problem extends stochastic bandits to a continuous action set defined over a metric space, where the expected reward function satisfies a Lipschitz condition.…
Low-rank Matrix Bandits with Heavy-tailed Rewards
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
In stochastic low-rank matrix bandit, the expected reward of an arm is equal to the inner product between its feature matrix and some unknown by low-rank parameter matr…
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
In the stochastic contextual low-rank matrix bandit problem, the expected reward of an action is given by the inner product between the action's feature matrix and some fixed, but…
Robust Lipschitz Bandits to Adversarial Corruptions
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
Lipschitz bandit is a variant of stochastic bandits that deals with a continuous arm set defined on a metric space, where the reward function is subject to a Lipschitz constraint.…
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits
Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee
In stochastic contextual bandits, an agent sequentially makes actions from a time-dependent action set based on past experience to minimize the cumulative regret. Like many other m…
A Review of Adversarial Attack and Defense for Classification Methods
Yao Li, Minhao Cheng, Cho-Jui Hsieh +1
Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many classification methods, especially deep neural networks (DNNs), are v…
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
Qin Ding, Yue Kang, Yi-Wei Liu +3
The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online adverti…
Adversarial Examples Detection with Bayesian Neural Network
Yao Li, Tongyi Tang, Cho-Jui Hsieh +1
In this paper, we propose a new framework to detect adversarial examples motivated by the observations that random components can improve the smoothness of predictors and make it e…
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Yao Li, Martin Renqiang Min, Wenchao Yu +3
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples. Inspired by the observation that the intrinsic dimension of i…
Block-wise Partitioning for Extreme Multi-label Classification
Yuefeng Liang, Cho-Jui Hsieh, Thomas C. M. Lee
Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set. Many existing solutions…