@article{zhang2023generate,title={To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images... For Now},author={Zhang*, Yimeng and Jia*, Jinghan and Chen, Xin and Chen, Aochuan and Zhang, Yihua and Liu, Jiancheng and Ding, Ke and Liu, Sijia},journal={arXiv preprint arXiv:2310.11868},year={2023},publisher={ECCV 2024},}
preprint
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
Aochuan Chen, Yimeng Zhang, Jinghan Jia, and
7 more authors
Zeroth-order (ZO) optimization has become a popular technique for solving machine learning (ML) problems when first-order (FO) information is difficult or impossible to obtain. However, the scalability of ZO optimization remains an open problem: Its use has primarily been limited to relatively small-scale ML problems, such as sample-wise adversarial attack generation. To our best knowledge, no prior work has demonstrated the effectiveness of ZO optimization in training deep neural networks (DNNs) without a significant decrease in performance. To overcome this roadblock, we develop DeepZero, a principled ZO deep learning (DL) framework that can scale ZO optimization to DNN training from scratch through three primary innovations. First, we demonstrate the advantages of coordinate-wise gradient estimation (CGE) over randomized vector-wise gradient estimation in training accuracy and computational efficiency. Second, we propose a sparsity-induced ZO training protocol that extends the model pruning methodology using only finite differences to explore and exploit the sparse DL prior in CGE. Third, we develop the methods of feature reuse and forward parallelization to advance the practical implementations of ZO training. Our extensive experiments show that DeepZero achieves state-of-the-art (SOTA) accuracy on ResNet-20 trained on CIFAR-10, approaching FO training performance for the first time. Furthermore, we show the practical utility of DeepZero in applications of certified adversarial defense and DL-based partial differential equation error correction, achieving 10-20% improvement over SOTA. We believe our results will inspire future research on scalable ZO optimization and contribute to advancing DL with black box.
@inproceedings{chen2023deepzero,title={DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training},author={Chen, Aochuan and Zhang, Yimeng and Jia, Jinghan and Diffenderfer, James and Liu, Jiancheng and Parasyris, Konstantinos and Zhang, Yihua and Zhang, Zheng and Kailkhura, Bhavya and Liu, Sijia},month=oct,year={2023}}
NeurIPS’23
Model sparsification can simplify machine unlearning
Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, and
5 more authors
@article{jia2023model,title={Model sparsification can simplify machine unlearning},author={Jia*, Jinghan and Liu*, Jiancheng and Ram, Parikshit and Yao, Yuguang and Liu, Gaowen and Liu, Yang and Sharma, Pranay and Liu, Sijia},journal={arXiv preprint arXiv:2304.04934},year={2023},publisher={NeurIPS 2023},}
NeurIPS’23
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Yihua Zhang, Yimeng Zhang, Aochuan Chen, and
6 more authors
In Thirty-seventh Conference on Neural Information Processing Systems Oct 2023
Massive data is often considered essential for deep learning applications, but it also incurs significant computational and infrastructural costs. Therefore, dataset pruning (DP) has emerged as an effective way to improve data efficiency by identifying and removing redundant training samples without sacrificing performance. In this work, we aim to address the problem of DP for transfer learning, i.e., how to prune a source dataset for improved pretraining efficiency and lossless finetuning accuracy on downstream target tasks. To our best knowledge, the problem of DP for transfer learning remains open, as previous studies have primarily addressed DP and transfer learning as separate problems. By contrast, we establish a unified viewpoint to integrate DP with transfer learning and find that existing DP methods are not suitable for the transfer learning paradigm. We then propose two new DP methods, label mapping and feature mapping, for supervised and self-supervised pretraining settings respectively, by revisiting the DP problem through the lens of source-target domain mapping. Furthermore, we demonstrate the effectiveness of our approach on numerous transfer learning tasks. We show that source data classes can be pruned by up to 40% without sacrificing the downstream performance, resulting in a significant 2 5 times speed-up during the pretraining stage. Besides, our proposal exhibits broad applicability and can improve other computationally intensive transfer learning techniques, such as adversarial pretraining.
@inproceedings{zhang2023selectivity,title={Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning},author={Zhang, Yihua and Zhang, Yimeng and Chen, Aochuan and Jia, Jinghan and Liu, Jiancheng and Liu, Gaowen and Hong, Mingyi and Chang, Shiyu and Liu, Sijia},booktitle={Thirty-seventh Conference on Neural Information Processing Systems},year={2023}}
SANER’23
CLAWSAT: Towards Both Robust and Accurate Code Models
Jinghan Jia*, Shashank Srikant*, Tamara Mitrovska, and
4 more authors
doi = {10.48550/ARXIV.2211.11711},author = {Jia*, Jinghan and Srikant*, Shashank and Mitrovska, Tamara and Gan, Chuang and Chang, Shiyu and Liu, Sijia and O'Reilly, Una-May},keywords = {Machine Learning (cs.LG), Programming Languages (cs.PL), Software Engineering (cs.SE), FOS: Computer and information sciences, FOS: Computer and information sciences},title = {CLAWSAT: Towards Both Robust and Accurate Code Models},year = {2023},copyright = {Creative Commons Attribution 4.0 International}}
2022
ICLR’22
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Yimeng Zhang, Yuguang Yao, Jinghan Jia, and
4 more authors
arXiv preprint arXiv:2203.14195 Oct 2022
TSRML’22
On the Robustness of deep learning-based MRI Reconstruction to image transformations
Jinghan Jia, Mingyi Hong, Yimeng Zhang, and
2 more authors
@article{jia2022robustness,title={On the Robustness of deep learning-based MRI Reconstruction to image transformations},author={Jia, Jinghan and Hong, Mingyi and Zhang, Yimeng and Ak{\c{c}}akaya, Mehmet and Liu, Sijia},journal={arXiv preprint arXiv:2211.04930},year={2022}}
2021
ISMRM
Instabilities in Conventional Multi-Coil MRI Reconstruction with Small Adversarial Perturbations
Chi Zhang*, Jinghan Jia*, Burhaneddin Yaman, and
4 more authors
In 2021 55th Asilomar Conference on Signals, Systems, and Computers Oct 2021
@inproceedings{zhang2021instabilities,title={Instabilities in Conventional Multi-Coil MRI Reconstruction with Small Adversarial Perturbations},author={Zhang*, Chi and Jia*, Jinghan and Yaman, Burhaneddin and Moeller, Steen and Liu, Sijia and Hong, Mingyi and Ak{\c{c}}akaya, Mehmet},booktitle={2021 55th Asilomar Conference on Signals, Systems, and Computers},pages={895--899},year={2021},organization={IEEE}}
Instabilities in Conventional Multi-Coil MRI Reconstruction with Small Adversarial PerturbationsIn 2021 55th Asilomar Conference on Signals, Systems, and Computers Oct 2021