1.8k citations · 3.4k across the 56 of their papers we have counts for
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Symmetric Cross Entropy for Robust Learning with Noisy Labels
Yisen Wang, Xingjun Ma, Zaiyi Chen +3
Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Though a number of approaches have been proposed for learning wi…
Characterizing Attacks on Deep Reinforcement Learning
Xinlei Pan, Chaowei Xiao, Warren He +8
Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations.…
Defending Against Adversarial Attacks Using Random Forests
Yifan Ding, Liqiang Wang, Huan Zhang +3
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world. Unfortun…
Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
Dong-Dong Chen, Yisen Wang, Jinfeng Yi +2
Unsupervised domain adaptation aims to transfer the classifier learned from the source domain to the target domain in an unsupervised manner. With the help of target pseudo-labels,…
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
Lu Wang, Xuanqing Liu, Jinfeng Yi +2
We study the problem of computing the minimum adversarial perturbation of the Nearest Neighbor (NN) classifiers. Previous attempts either conduct attacks on continuous approximatio…
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss
Pengcheng Li, Jinfeng Yi, Bowen Zhou +1
Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we improve the robustness of DNNs by utilizing techniques of…
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
Xinlei Pan, Weiyao Wang, Xiaoshuai Zhang +3
Machine learning has been widely applied to various applications, some of which involve training with privacy-sensitive data. A modest number of data breaches have been studied, in…
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
Lan-Zhe Guo, Yu-Feng Li, Ming Li +3
Weakly supervised data are widespread and have attracted much attention. However, since label quality is often difficult to guarantee, sometimes the use of weakly supervised data w…