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ORCIDSijia Liu 0001
Person information
- affiliation: Michigan State University, Department of Computer Science and Engineering, East Lansing, USA
- affiliation (2018 - 2020): IBM Research, MIT-IBM Watson AI Lab, Cambridge, MA, USA
- affiliation (2016 - 2017): University of Michigan, Ann Arbor, MI, USA
- affiliation (PhD 2016): Syracuse University, NY, USA
Other persons with the same name
- Sijia Liu — disambiguation page
- Sijia Liu 0002
![[0000-0001-9763-1164]](https://dblp.org/img/orcid-mark.12x12.png "0000-0001-9763-1164")
— Mayo Clinic, Rochester, MN, USA (and 1 more) - Sijia Liu 0003 — Beijing Jiaotong University, School of Electrical Engineering, China
- Sijia Liu 0004 — Zhengzhou Information Science and Technology Institute, Zhengzhou, China
- Sijia Liu 0005 — BYD Lithium Battery Company, Ltd., Shenzhen, China (and 1 more)
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2020 – today
- 2023
[c124]Pin-Yu Chen, Sijia Liu:
Holistic Adversarial Robustness of Deep Learning Models. AAAI 2023: 15411-15420- [c123]Sijia Liu:
AAAI New Faculty Highlights: General and Scalable Optimization for Robust AI. AAAI 2023: 15447 - [c122]Soumyadeep Pal, Ren Wang, Yuguang Yao, Sijia Liu:
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning. SafeAI@AAAI 2023 - [c121]Yimeng Zhang, Akshay Karkal Kamath, Qiucheng Wu, Zhiwen Fan, Wuyang Chen, Zhangyang Wang, Shiyu Chang, Sijia Liu, Cong Hao:
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices. ASP-DAC 2023: 745-750 - [c120]Sijia Liu, Jiahao Liu, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, Ning Gu:
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation. CIKM 2023: 1493-1502 - [c119]Ren Wang, Yuxuan Li, Sijia Liu:
Exploring Diversified Adversarial Robustness in Neural Networks via Robust Mode Connectivity. CVPR Workshops 2023: 2346-2352 - [c118]Haomin Zhuang, Yihua Zhang, Sijia Liu:
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion. CVPR Workshops 2023: 2385-2392 - [c117]Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding:
Text-Visual Prompting for Efficient 2D Temporal Video Grounding. CVPR 2023: 14794-14804 - [c116]Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang, Sijia Liu:
Understanding and Improving Visual Prompting: A Label-Mapping Perspective. CVPR 2023: 19133-19143 - [c115]Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen, Sijia Liu:
Visual Prompting for Adversarial Robustness. ICASSP 2023: 1-5 - [c114]Jinghan Jia, Yihua Zhang, Dogyoon Song, Sijia Liu, Alfred O. Hero III:
Robustness-Preserving Lifelong Learning Via Dataset Condensation. ICASSP 2023: 1-5 - [c113]Hui Li, Jinghan Jia, Shijun Liang, Yuguang Yao, Saiprasad Ravishankar, Sijia Liu:
SMUG: Towards Robust Mri Reconstruction by Smoothed Unrolling. ICASSP 2023: 1-5 - [c112]Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, Miao Liu:
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks. ICLR 2023 - [c111]Bairu Hou, Jinghan Jia, Yihua Zhang, Guanhua Zhang, Yang Zhang, Sijia Liu, Shiyu Chang:
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization. ICLR 2023 - [c110]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen:
A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity. ICLR 2023 - [c109]Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush R. Varshney, Sijia Liu:
What Is Missing in IRM Training and Evaluation? Challenges and Solutions. ICLR 2023 - [c108]Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen:
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. ICML 2023: 6074-6114 - [c107]Prashant Khanduri, Ioannis C. Tsaknakis, Yihua Zhang, Jia Liu, Sijia Liu, Jiawei Zhang, Mingyi Hong:
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach. ICML 2023: 16291-16325 - [c106]Jinghan Jia, Shashank Srikant, Tamara Mitrovska, Chuang Gan, Shiyu Chang, Sijia Liu, Una-May O'Reilly:
ClawSAT: Towards Both Robust and Accurate Code Models. SANER 2023: 212-223 - [i130]Soumyadeep Pal, Ren Wang, Yuguang Yao, Sijia Liu:
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning. CoRR abs/2301.08751 (2023) - [i129]Alex Gu, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Certified Interpretability Robustness for Class Activation Mapping. CoRR abs/2301.11324 (2023) - [i128]Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, Miao Liu:
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks. CoRR abs/2302.02922 (2023) - [i127]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen:
A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity. CoRR abs/2302.06015 (2023) - [i126]Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush R. Varshney, Sijia Liu:
What Is Missing in IRM Training and Evaluation? Challenges and Solutions. CoRR abs/2303.02343 (2023) - [i125]Jinghan Jia, Yihua Zhang, Dogyoon Song, Sijia Liu, Alfred O. Hero III:
Robustness-preserving Lifelong Learning via Dataset Condensation. CoRR abs/2303.04183 (2023) - [i124]Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding:
Text-Visual Prompting for Efficient 2D Temporal Video Grounding. CoRR abs/2303.04995 (2023) - [i123]Yuguang Yao, Jiancheng Liu, Yifan Gong, Xiaoming Liu, Yanzhi Wang, Xue Lin, Sijia Liu:
Can Adversarial Examples Be Parsed to Reveal Victim Model Information? CoRR abs/2303.07474 (2023) - [i122]Ren Wang, Yuxuan Li, Sijia Liu:
Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified 𝓁p Attacks. CoRR abs/2303.10225 (2023) - [i121]Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu:
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR abs/2303.12291 (2023) - [i120]Hui Li, Jinghan Jia, Shijun Liang, Yuguang Yao, Saiprasad Ravishankar, Sijia Liu:
SMUG: Towards robust MRI reconstruction by smoothed unrolling. CoRR abs/2303.12735 (2023) - [i119]Haomin Zhuang, Yihua Zhang, Sijia Liu:
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion. CoRR abs/2303.16378 (2023) - [i118]Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu:
Model Sparsification Can Simplify Machine Unlearning. CoRR abs/2304.04934 (2023) - [i117]Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen:
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. CoRR abs/2306.04073 (2023) - [i116]Tong Steven Sun, Yuyang Gao, Shubham Khaladkar, Sijia Liu, Liang Zhao, Young-Ho Kim, Sungsoo Ray Hong:
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations. CoRR abs/2307.04036 (2023) - [i115]Zhen Zhang, Guanhua Zhang, Bairu Hou, Wenqi Fan, Qing Li, Sijia Liu, Yang Zhang, Shiyu Chang:
Certified Robustness for Large Language Models with Self-Denoising. CoRR abs/2307.07171 (2023) - [i114]Yihua Zhang, Prashant Khanduri, Ioannis C. Tsaknakis, Yuguang Yao, Mingyi Hong, Sijia Liu:
An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning. CoRR abs/2308.00788 (2023) - [i113]Sijia Liu, Jiahao Liu, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, Ning Gu:
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation. CoRR abs/2308.06878 (2023) - [i112]Yequan Zhao, Xinling Yu, Zhixiong Chen, Ziyue Liu, Sijia Liu, Zheng Zhang:
Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks. CoRR abs/2308.09858 (2023) - [i111]Yihua Zhang, Ruisi Cai, Tianlong Chen, Guanhua Zhang, Huan Zhang, Pin-Yu Chen, Shiyu Chang, Zhangyang Wang, Sijia Liu:
Robust Mixture-of-Expert Training for Convolutional Neural Networks. CoRR abs/2308.10110 (2023) - [i110]Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Jiancheng Liu, Konstantinos Parasyris, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu:
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training. CoRR abs/2310.02025 (2023) - [i109]Yihua Zhang, Yimeng Zhang, Aochuan Chen, Jinghan Jia, Jiancheng Liu, Gaowen Liu, Mingyi Hong, Shiyu Chang, Sijia Liu:
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning. CoRR abs/2310.08782 (2023) - [i108]Yimeng Zhang, Jinghan Jia, Xin Chen, Aochuan Chen, Yihua Zhang, Jiancheng Liu, Ke Ding, Sijia Liu:
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now. CoRR abs/2310.11868 (2023) - [i107]Chongyu Fan, Jiancheng Liu, Yihua Zhang, Dennis Wei, Eric Wong, Sijia Liu:
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. CoRR abs/2310.12508 (2023) - [i106]Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury:
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. CoRR abs/2310.16173 (2023) - [i105]Zhuoshi Pan, Yuguang Yao, Gaowen Liu, Bingquan Shen, H. Vicky Zhao, Ramana Rao Kompella, Sijia Liu:
From Trojan Horses to Castle Walls: Unveiling Bilateral Backdoor Effects in Diffusion Models. CoRR abs/2311.02373 (2023) - [i104]Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Sijia Liu, James A. Hendler, Dakuo Wang:
More Samples or More Prompt Inputs? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering. CoRR abs/2311.09782 (2023) - 2022
- [j20]Ren Wang, Tianqi Chen, Philip Yao, Sijia Liu, Indika Rajapakse, Alfred O. Hero III:
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense. IEEE Access 10: 103074-103088 (2022) - [j19]Ao Liu, Xiaoyu Chen, Sijia Liu, Lirong Xia, Chuang Gan:
Certifiably robust interpretation via Rényi differential privacy. Artif. Intell. 313: 103787 (2022) - [j18]Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang:
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning. Trans. Mach. Learn. Res. 2022 (2022) - [j17]Tianlong Chen, Zhenyu Zhang, Jun Wu, Randy Huang, Sijia Liu, Shiyu Chang, Zhangyang Wang:
Can You Win Everything with A Lottery Ticket? Trans. Mach. Learn. Res. 2022 (2022) - [j16]Tianyun Zhang, Shaokai Ye, Xiaoyu Feng, Xiaolong Ma, Kaiqi Zhang, Zhengang Li, Jian Tang, Sijia Liu, Xue Lin, Yongpan Liu, Makan Fardad, Yanzhi Wang:
StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs. IEEE Trans. Neural Networks Learn. Syst. 33(5): 2259-2273 (2022) - [j15]Yifan Gong, Geng Yuan, Zheng Zhan, Wei Niu, Zhengang Li, Pu Zhao, Yuxuan Cai, Sijia Liu, Bin Ren, Xue Lin, Xulong Tang, Yanzhi Wang:
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration. ACM Trans. Design Autom. Electr. Syst. 27(5): 47:1-47:26 (2022) - [c105]Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Chia-Mu Yu:
Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning. AAAI 2022: 6926-6934 - [c104]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Zeroth-Order Optimization for Composite Problems with Functional Constraints. AAAI 2022: 7453-7461 - [c103]Tianlong Chen, Zhenyu Zhang, Yihua Zhang, Shiyu Chang, Sijia Liu, Zhangyang Wang:
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free. CVPR 2022: 588-599 - [c102]Vishal Asnani, Xi Yin, Tal Hassner, Sijia Liu, Xiaoming Liu:
Proactive Image Manipulation Detection. CVPR 2022: 15365-15374 - [c101]Vardaan Taneja, Pin-Yu Chen, Yuguang Yao, Sijia Liu:
When Does Backdoor Attack Succeed in Image Reconstruction? A Study of Heuristics vs. Bi-Level Solution. ICASSP 2022: 4398-4402 - [c100]Yifan Gong, Yuguang Yao, Yize Li, Yimeng Zhang, Xiaoming Liu, Xue Lin, Sijia Liu:
Reverse Engineering of Imperceptible Adversarial Image Perturbations. ICLR 2022 - [c99]Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis. ICLR 2022 - [c98]Tianshu Huang, Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang:
Optimizer Amalgamation. ICLR 2022 - [c97]Prashant Khanduri, Haibo Yang, Mingyi Hong, Jia Liu, Hoi-To Wai, Sijia Liu:
Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach. ICLR 2022 - [c96]Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jinfeng Yi, Mingyi Hong, Shiyu Chang, Sijia Liu:
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective. ICLR 2022 - [c95]Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang:
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training. ICML 2022: 3747-3759 - [c94]Tianlong Chen, Huan Zhang, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, Zhangyang Wang:
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness. ICML 2022: 3760-3772 - [c93]Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng:
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. ICML 2022: 11387-11412 - [c92]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. ICML 2022: 13014-13051 - [c91]Yihua Zhang, Guanhua Zhang, Prashant Khanduri, Mingyi Hong, Shiyu Chang, Sijia Liu:
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization. ICML 2022: 26693-26712 - [c90]Pu Zhao, Parikshit Ram, Songtao Lu, Yuguang Yao, Djallel Bouneffouf, Xue Lin, Sijia Liu:
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations. IJCAI 2022: 1714-1720 - [c89]Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu:
The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022). KDD 2022: 4858-4859 - [c88]Yong Xie, Dakuo Wang, Pin-Yu Chen, Jinjun Xiong, Sijia Liu, Oluwasanmi Koyejo:
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction. NAACL-HLT 2022: 587-599 - [c87]Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu:
Advancing Model Pruning via Bi-level Optimization. NeurIPS 2022 - [c86]Guanhua Zhang, Yihua Zhang, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, Shiyu Chang:
Fairness Reprogramming. NeurIPS 2022 - [c85]Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu:
Distributed adversarial training to robustify deep neural networks at scale. UAI 2022: 2353-2363 - [i103]Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis. CoRR abs/2201.08514 (2022) - [i102]Pin-Yu Chen, Sijia Liu:
Holistic Adversarial Robustness of Deep Learning Models. CoRR abs/2202.07201 (2022) - [i101]Tianshu Huang, Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang:
Optimizer Amalgamation. CoRR abs/2203.06474 (2022) - [i100]Yifan Gong, Yuguang Yao, Yize Li, Yimeng Zhang, Xiaoming Liu, Xue Lin, Sijia Liu:
Reverse Engineering of Imperceptible Adversarial Image Perturbations. CoRR abs/2203.14145 (2022) - [i99]Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jinfeng Yi, Mingyi Hong, Shiyu Chang, Sijia Liu:
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective. CoRR abs/2203.14195 (2022) - [i98]Vishal Asnani, Xi Yin, Tal Hassner, Sijia Liu, Xiaoming Liu:
Proactive Image Manipulation Detection. CoRR abs/2203.15880 (2022) - [i97]Quanfu Fan, Yilai Li, Yuguang Yao, John Cohn, Sijia Liu, Seychelle M. Vos, Michael A. Cianfrocco:
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection. CoRR abs/2204.07543 (2022) - [i96]Yong Xie, Dakuo Wang, Pin-Yu Chen, Jinjun Xiong, Sijia Liu, Sanmi Koyejo:
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction. CoRR abs/2205.01094 (2022) - [i95]Tianlong Chen, Zhenyu Zhang, Yihua Zhang, Shiyu Chang, Sijia Liu, Zhangyang Wang:
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free. CoRR abs/2205.11819 (2022) - [i94]Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong:
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning. CoRR abs/2206.02785 (2022) - [i93]Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang:
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training. CoRR abs/2206.04762 (2022) - [i92]Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu:
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale. CoRR abs/2206.06257 (2022) - [i91]Tianlong Chen, Huan Zhang, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, Zhangyang Wang:
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness. CoRR abs/2206.07839 (2022) - [i90]Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang:
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning. CoRR abs/2206.07842 (2022) - [i89]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling. CoRR abs/2207.03584 (2022) - [i88]Guanhua Zhang, Yihua Zhang, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, Shiyu Chang:
Fairness Reprogramming. CoRR abs/2209.10222 (2022) - [i87]Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu:
Advancing Model Pruning via Bi-level Optimization. CoRR abs/2210.04092 (2022) - [i86]Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen, Sijia Liu:
Visual Prompting for Adversarial Robustness. CoRR abs/2210.06284 (2022) - [i85]Yimeng Zhang, Akshay Karkal Kamath, Qiucheng Wu, Zhiwen Fan, Wuyang Chen, Zhangyang Wang, Shiyu Chang, Sijia Liu, Cong Hao:
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices. CoRR abs/2210.08578 (2022) - [i84]Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang, Sijia Liu:
Understanding and Improving Visual Prompting: A Label-Mapping Perspective. CoRR abs/2211.11635 (2022) - [i83]Jinghan Jia, Shashank Srikant, Tamara Mitrovska, Chuang Gan, Shiyu Chang, Sijia Liu, Una-May O'Reilly:
CLAWSAT: Towards Both Robust and Accurate Code Models. CoRR abs/2211.11711 (2022) - [i82]Bairu Hou, Jinghan Jia, Yihua Zhang, Guanhua Zhang, Yang Zhang, Sijia Liu, Shiyu Chang:
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization. CoRR abs/2212.09254 (2022) - [i81]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization. CoRR abs/2212.09513 (2022) - 2021
- [j14]Shuai Zhang, Meng Wang, Jinjun Xiong, Sijia Liu, Pin-Yu Chen:
Improved Linear Convergence of Training CNNs With Generalizability Guarantees: A One-Hidden-Layer Case. IEEE Trans. Neural Networks Learn. Syst. 32(6): 2622-2635 (2021) - [c84]Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Luca Daniel:
Fast Training of Provably Robust Neural Networks by SingleProp. AAAI 2021: 6803-6811 - [c83]Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das:
Self-Progressing Robust Training. AAAI 2021: 7107-7115 - [c82]Wei Niu, Mengshu Sun, Zhengang Li, Jou-An Chen, Jiexiong Guan, Xipeng Shen, Yanzhi Wang, Sijia Liu, Xue Lin, Bin Ren:
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices. AAAI 2021: 9179-9187 - [c81]Chi Zhang, Jinghan Jia, Burhaneddin Yaman, Steen Moeller, Sijia Liu, Mingyi Hong, Mehmet Akçakaya:
Instabilities in Conventional Multi-Coil MRI Reconstruction with Small Adversarial Perturbations. ACSCC 2021: 895-899 - [c80]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization. AISTATS 2021: 2170-2178 - [c79]Jeet Mohapatra, Ching-Yun Ko, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Hidden Cost of Randomized Smoothing. AISTATS 2021: 4033-4041 - [c78]Zhengang Li, Geng Yuan, Wei Niu, Pu Zhao, Yanyu Li, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Zhiyu Chen, Sijia Liu, Kaiyuan Yang, Bin Ren, Yanzhi Wang, Xue Lin:
NPAS: A Compiler-Aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration. CVPR 2021: 14255-14266 - [c77]Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang:
The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models. CVPR 2021: 16306-16316 - [c76]Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Sijia Liu, Yanzhi Wang, Xue Lin:
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions. ICCV 2021: 5231-5240 - [c75]Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, Meng Wang:
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning. ICLR 2021 - [c74]Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang:
Robust Overfitting may be mitigated by properly learned smoothening. ICLR 2021 - [c73]Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, Zhangyang Wang:
Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning. ICLR 2021 - [c72]Shashank Srikant, Sijia Liu, Tamara Mitrovska, Shiyu Chang, Quanfu Fan, Gaoyuan Zhang, Una-May O'Reilly:
Generating Adversarial Computer Programs using Optimized Obfuscations. ICLR 2021 - [c71]Ning Liu, Geng Yuan, Zhengping Che, Xuan Shen, Xiaolong Ma, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, Yanzhi Wang:
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not? ICML 2021: 7011-7020 - [c70]Wei Niu, Zhenglun Kong, Geng Yuan, Weiwen Jiang, Jiexiong Guan, Caiwen Ding, Pu Zhao, Sijia Liu, Bin Ren, Yanzhi Wang:
A Compression-Compilation Framework for On-mobile Real-time BERT Applications. IJCAI 2021: 5000-5003 - [c69]Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu:
Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). KDD 2021: 4112-4113 - [c68]Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong:
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks. NeurIPS 2021: 2707-2720 - [c67]