19 citations · 59 across the 15 of their papers we have counts for
10 papers · 1 filter
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Yihua Zhang, Yimeng Zhang, Aochuan Chen +6
Massive data is often considered essential for deep learning applications, but it also incurs significant computational and infrastructural costs. Therefore, dataset pruning (DP) h…
CLAWSAT: Towards Both Robust and Accurate Code Models
Jinghan Jia, Shashank Srikant, Tamara Mitrovska +4
We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models. Different from existing works, we show that code obfusca…
Fairness Reprogramming
Guanhua Zhang, Yihua Zhang, Yang Zhang +4
Despite a surge of recent advances in promoting machine Learning (ML) fairness, the existing mainstream approaches mostly require retraining or finetuning the entire weights of the…
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning
Tianlong Chen, Sijia Liu, Shiyu Chang +2
Class-incremental learning (CIL) suffers from the notorious dilemma between learning newly added classes and preserving previously learned class knowledge. That catastrophic forget…
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Tianlong Chen, Huan Zhang, Zhenyu Zhang +4
Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish. Th…
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Tianlong Chen, Zhenyu Zhang, Sijia Liu +3
Pre-training serves as a broadly adopted starting point for transfer learning on various downstream tasks. Recent investigations of lottery tickets hypothesis (LTH) demonstrate suc…
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free
Tianlong Chen, Zhenyu Zhang, Yihua Zhang +3
Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a particular t…
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Yimeng Zhang, Yuguang Yao, Jinghan Jia +4
The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustif…
Optimizer Amalgamation
Tianshu Huang, Tianlong Chen, Sijia Liu +3
Selecting an appropriate optimizer for a given problem is of major interest for researchers and practitioners. Many analytical optimizers have been proposed using a variety of theo…
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
Yihua Zhang, Guanhua Zhang, Prashant Khanduri +3
Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness of deep neural networks against adversarial attacks. It is built on min-max optimization (…