58 citations · 305 across the 33 of their papers we have counts for
9 papers · 1 filter
Clustering Effect of (Linearized) Adversarial Robust Models
Yang Bai, Xin Yan, Yong Jiang +2
Adversarial robustness has received increasing attention along with the study of adversarial examples. So far, existing works show that robust models not only obtain robustness aga…
Improving Adversarial Robustness via Channel-wise Activation Suppressing
Yang Bai, Yuyuan Zeng, Yong Jiang +3
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing…
Improving Query Efficiency of Black-box Adversarial Attack
Yang Bai, Yuyuan Zeng, Yong Jiang +3
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the…
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning
Yiming Li, Jiawang Bai, Jiawei Li +3
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality. In this paper, we propose a knowledge distilla…
Toward Adversarial Robustness via Semi-supervised Robust Training
Yiming Li, Baoyuan Wu, Yan Feng +4
Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) thro…
Automatic Financial Feature Construction
Jie Fang, Shutao Xia, Jianwu Lin +1
In automatic financial feature construction task, the state-of-the-art technic leverages reverse polish expression to represent the features, then use genetic programming (GP) to c…
Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
Jiawang Bai, Yiming Li, Jiawei Li +2
How to obtain a model with good interpretability and performance has always been an important research topic. In this paper, we propose rectified decision trees (ReDT), a knowledge…
Multinomial Random Forest: Toward Consistency and Privacy-Preservation
Yiming Li, Jiawang Bai, Jiawei Li +4
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed mu…
Fully Implicit Online Learning
Chaobing Song, Ji Liu, Han Liu +2
Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ( implicit update) is known to be beneficial t…