1.8k citations · 9.5k across the 166 of their papers we have counts for
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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…
Label Disentanglement in Partition-based Extreme Multilabel Classification
Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu +2
Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e.g., millions or more). However…
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…
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Stochastic linear contextual bandit algorithms have substantial applications in practice, such as recommender systems, online advertising, clinical trials, etc. Recent works show t…
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…
Multiscale Non-stationary Stochastic Bandits
Qin Ding, Cho-Jui Hsieh, James Sharpnack
Classic contextual bandit algorithms for linear models, such as LinUCB, assume that the reward distribution for an arm is modeled by a stationary linear regression. When the linear…
Enhancing Certifiable Robustness via a Deep Model Ensemble
Huan Zhang, Minhao Cheng, Cho-Jui Hsieh
We propose an algorithm to enhance certified robustness of a deep model ensemble by optimally weighting each base model. Unlike previous works on using ensembles to empirically imp…
The Limitations of Adversarial Training and the Blind-Spot Attack
Huan Zhang, Hongge Chen, Zhao Song +3
The adversarial training procedure proposed by Madry et al. (2018) is one of the most effective methods to defend against adversarial examples in deep neural networks (DNNs). In ou…
RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications
Huan Zhang, Pengchuan Zhang, Cho-Jui Hsieh
The Jacobian matrix (or the gradient for single-output networks) is directly related to many important properties of neural networks, such as the function landscape, stationary poi…
Stochastic Zeroth-order Optimization via Variance Reduction method
Liu Liu, Minhao Cheng, Cho-Jui Hsieh +1
Derivative-free optimization has become an important technique used in machine learning for optimizing black-box models. To conduct updates without explicitly computing gradient, m…
Towards Fast Computation of Certified Robustness for ReLU Networks
Tsui-Wei Weng, Huan Zhang, Hongge Chen +5
Verifying the robustness property of a general Rectified Linear Unit (ReLU) network is an NP-complete problem [Katz, Barrett, Dill, Julian and Kochenderfer CAV17]. Although finding…
SQL-Rank: A Listwise Approach to Collaborative Ranking
Liwei Wu, Cho-Jui Hsieh, James Sharpnack
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to pre…
History PCA: A New Algorithm for Streaming PCA
Puyudi Yang, Cho-Jui Hsieh, Jane-Ling Wang
In this paper we propose a new algorithm for streaming principal component analysis. With limited memory, small devices cannot store all the samples in the high-dimensional regime.…
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen +5
The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually impercep…
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Pin-Yu Chen, Yash Sharma, Huan Zhang +2
Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples - a visually indistinguishable adversarial image can easily be crafted to c…
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Pin-Yu Chen, Huan Zhang, Yash Sharma +2
Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not l…
GPU-acceleration for Large-scale Tree Boosting
Huan Zhang, Si Si, Cho-Jui Hsieh
In this paper, we present a novel massively parallel algorithm for accelerating the decision tree building procedure on GPUs (Graphics Processing Units), which is a crucial step in…