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Transfer Metric Learning: Algorithms, Applications and Outlooks
Yong Luo, Yonggang Wen, Ling-Yu Duan +1
Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data min…
An Optimal Transport View on Generalization
Jingwei Zhang, Tongliang Liu, Dacheng Tao
We derive upper bounds on the generalization error of learning algorithms based on their \emph{algorithmic transport cost}: the expected Wasserstein distance between the output hyp…
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao
Here we propose a general theoretical method for analyzing the risk bound in the presence of adversaries. Specifically, we try to fit the adversarial learning problem into the mini…
Towards Query Efficient Black-box Attacks: An Input-free Perspective
Yali Du, Meng Fang, Jinfeng Yi +2
Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack…
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…
An Information-Theoretic View for Deep Learning
Jingwei Zhang, Tongliang Liu, Dacheng Tao
Deep learning has transformed computer vision, natural language processing, and speech recognition\cite{badrinarayanan2017segnet, dong2016image, ren2017faster, ji20133d}. However,…