3 citations · 9 across the 5 of their papers we have counts for
5 papers
Batch Active Learning from the Perspective of Sparse Approximation
Maohao Shen, Bowen Jiang, Jacky Yibo Zhang +1
Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formul…
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization
Xiaojun Xu, Jacky Yibo Zhang, Evelyn Ma +3
Machine learning (ML) robustness and domain generalization are fundamentally correlated: they essentially concern data distribution shifts under adversarial and natural settings, r…
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data…
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability
Kaizhao Liang, Jacky Y. Zhang, Boxin Wang +3
Knowledge transferability, or transfer learning, has been widely adopted to allow a pre-trained model in the source domain to be effectively adapted to downstream tasks in the targ…
Learning Sparse Distributions using Iterative Hard Thresholding
Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis +1
Iterative hard thresholding (IHT) is a projected gradient descent algorithm, known to achieve state of the art performance for a wide range of structured estimation problems, such…