1.8k citations · 9.5k across the 166 of their papers we have counts for
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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…
Efficient Neural Network Robustness Certification with General Activation Functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen +2
Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Neverthe…
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
Patrick H. Chen, Si Si, Sanjiv Kumar +2
Neural language models have been widely used in various NLP tasks, including machine translation, next word prediction and conversational agents. However, it is challenging to depl…
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen +3
CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extreme Value Theory (EVT) based robustness score for large-scale deep neural networks (DNNs). In this paper, we…
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…
Attack Graph Convolutional Networks by Adding Fake Nodes
Xiaoyun Wang, Minhao Cheng, Joe Eaton +2
In this paper, we study the robustness of graph convolutional networks (GCNs). Previous work have shown that GCNs are vulnerable to adversarial perturbation on adjacency or feature…
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
Xuanqing Liu, Yao Li, Chongruo Wu +1
We present a new algorithm to train a robust neural network against adversarial attacks. Our algorithm is motivated by the following two ideas. First, although recent work has demo…
Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient
Liu Liu, Xuanqing Liu, Cho-Jui Hsieh +1
Trust region and cubic regularization methods have demonstrated good performance in small scale non-convex optimization, showing the ability to escape from saddle points. Each iter…
Stochastically Controlled Stochastic Gradient for the Convex and Non-convex Composition problem
Liu Liu, Ji Liu, Cho-Jui Hsieh +1
In this paper, we consider the convex and non-convex composition problem with the structure , where $G( x )=\frac{1}{n}\sum\…
Fast Variance Reduction Method with Stochastic Batch Size
Xuanqing Liu, Cho-Jui Hsieh
In this paper we study a family of variance reduction methods with randomized batch size---at each step, the algorithm first randomly chooses the batch size and then selects a batc…
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
Jiarui Fang, Haohuan Fu, Guangwen Yang +1
Data parallelism has become a dominant method to scale Deep Neural Network (DNN) training across multiple nodes. Since synchronizing a large number of gradients of the local model…
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
Minhao Cheng, Thong Le, Pin-Yu Chen +3
We study the problem of attacking a machine learning model in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to…
Rob-GAN: Generator, Discriminator, and Adversarial Attacker
Xuanqing Liu, Cho-Jui Hsieh
We study two important concepts in adversarial deep learning---adversarial training and generative adversarial network (GAN). Adversarial training is the technique used to improve…
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Patrick H. Chen, Si Si, Yang Li +2
Model compression is essential for serving large deep neural nets on devices with limited resources or applications that require real-time responses. As a case study, a state-of-th…
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh +2
We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalabl…
LearningWord Embeddings for Low-resource Languages by PU Learning
Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh +1
Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for…
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…
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks
Chun-Chen Tu, Paishun Ting, Pin-Yu Chen +5
Recent studies have shown that adversarial examples in state-of-the-art image classifiers trained by deep neural networks (DNN) can be easily generated when the target model is tra…
Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems
Yang You, James Demmel, Cho-Jui Hsieh +1
We propose two new methods to address the weak scaling problems of KRR: the Balanced KRR (BKRR) and K-means KRR (KKRR). These methods consider alternative ways to partition the inp…
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…
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
Minhao Cheng, Jinfeng Yi, Pin-Yu Chen +2
Crafting adversarial examples has become an important technique to evaluate the robustness of deep neural networks (DNNs). However, most existing works focus on attacking the image…
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.…