1.8k citations · 3.3k across the 62 of their papers we have counts for
112 papers
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Li-Cheng Lan, Huan Zhang, Ti-Rong Wu +3
The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large…
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning
Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh
Intentionally crafted adversarial samples have effectively exploited weaknesses in deep neural networks. A standard method in adversarial robustness assumes a framework to defend a…
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation
Fan Yin, Yao Li, Cho-Jui Hsieh +1
Adversarial Examples Detection (AED) is a crucial defense technique against adversarial attacks and has drawn increasing attention from the Natural Language Processing (NLP) commun…
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection
Andrew Bai, Cho-Jui Hsieh, Wendy Kan +1
Generative adversarial network (GAN) continues to be a popular research direction due to its high generation quality. It is observed that many state-of-the-art GANs generate sample…
Uncertainty in Extreme Multi-label Classification
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhong +2
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain ha…
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation
Zhouxing Shi, Yihan Wang, Huan Zhang +2
Lipschitz constants are connected to many properties of neural networks, such as robustness, fairness, and generalization. Existing methods for computing Lipschitz constants either…