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Sijia 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
— 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]