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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)
- Sijia Liu 0006 — City University of Hong Kong, Hong Kong, SAR, China
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2020 – today
- 2024
[j25]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization. Comput. Optim. Appl. 87(1): 117-147 (2024)- [j24]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Correction to: Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization. Comput. Optim. Appl. 89(2): 575-578 (2024) - [j23]Yuguang Yao, Xiao Guo, Vishal Asnani, Yifan Gong, Jiancheng Liu, Xue Lin, Xiaoming Liu, Sijia Liu:
Reverse Engineering of Deceptions on Machine- and Human-Centric Attacks. Found. Trends Priv. Secur. 6(2): 53-152 (2024) - [j22]Yihua Zhang, Prashant Khanduri, Ioannis C. Tsaknakis, Yuguang Yao, Mingyi Hong, Sijia Liu:
An Introduction to Bilevel Optimization: Foundations and applications in signal processing and machine learning. IEEE Signal Process. Mag. 41(1): 38-59 (2024) - [c147]Chongyu Fan, Jiancheng Liu, Alfred Olivier Hero, Sijia Liu:
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning. ECCV (21) 2024: 278-297 - [c146]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. ECCV (57) 2024: 385-403 - [c145]Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu:
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. EMNLP 2024: 4276-4292 - [c144]Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng:
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints. EMNLP (Findings) 2024: 7773-7812 - [c143]Brian Zhang, Yuguang Yao, Sijia Liu:
Elevating Visual Prompting in Transfer Learning Via Pruned Model Ensembles: No Retrain, No Pain. ICASSP 2024: 6000-6004 - [c142]Aochuan Chen, Yimeng Zhang, Jinghan Jia, James Diffenderfer, Konstantinos Parasyris, Jiancheng Liu, Yihua Zhang, Zheng Zhang, Bhavya Kailkhura, Sijia Liu:
DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training. ICLR 2024 - [c141]Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu:
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation. ICLR 2024 - [c140]Soumyadeep Pal, Yuguang Yao, Ren Wang, Bingquan Shen, Sijia Liu:
Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency. ICLR 2024 - [c139]Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, Pin-Yu Chen:
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding. ICML 2024 - [c138]Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen:
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark. ICML 2024 - [c137]Hongkang Li, Meng Wang, Shuai Zhang, Sijia Liu, Pin-Yu Chen:
Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis. SAM 2024: 1-5 - [c136]Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang:
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing. NAACL (Short Papers) 2024: 246-257 - [c135]Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James A. Hendler, Dakuo Wang:
More Samples or More Prompts? Exploring Effective Few-Shot In-Context Learning for LLMs with In-Context Sampling. NAACL-HLT (Findings) 2024: 1772-1790 - [c134]Jiabao Ji, Yujian Liu, Yang Zhang, Gaowen Liu, Ramana Kompella, Sijia Liu, Shiyu Chang:
Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference. NeurIPS 2024 - [c133]Quanfu Fan, Yilai Li, Yuguang Yao, John Cohn, Sijia Liu, Ziping Xu, Seychelle M. Vos, Michael A. Cianfrocco:
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection. WACV 2024: 7877-7887 - [i157]Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Xiaojun Xu, Yuguang Yao, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu:
Rethinking Machine Unlearning for Large Language Models. CoRR abs/2402.08787 (2024) - [i156]Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen:
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark. CoRR abs/2402.11592 (2024) - [i155]Yihua Zhang, Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jiancheng Liu, Xiaoming Liu, Sijia Liu:
UnlearnCanvas: A Stylized Image Dataset to Benchmark Machine Unlearning for Diffusion Models. CoRR abs/2402.11846 (2024) - [i154]Hongkang Li, Shuai Zhang, Yihua Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen:
How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance. CoRR abs/2403.07310 (2024) - [i153]Chongyu Fan, Jiancheng Liu, Alfred O. Hero III, Sijia Liu:
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning. CoRR abs/2403.07362 (2024) - [i152]Soumyadeep Pal, Yuguang Yao, Ren Wang, Bingquan Shen, Sijia Liu:
Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency. CoRR abs/2403.10717 (2024) - [i151]Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang:
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing. CoRR abs/2404.12274 (2024) - [i150]Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu:
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning. CoRR abs/2404.18239 (2024) - [i149]Yuguang Yao, Steven A. Grosz, Sijia Liu, Anil K. Jain:
Hide and Seek: How Does Watermarking Impact Face Recognition? CoRR abs/2404.18890 (2024) - [i148]Yimeng Zhang, Xin Chen, Jinghan Jia, Yihua Zhang, Chongyu Fan, Jiancheng Liu, Mingyi Hong, Ke Ding, Sijia Liu:
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models. CoRR abs/2405.15234 (2024) - [i147]Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, Pin-Yu Chen:
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding. CoRR abs/2406.01977 (2024) - [i146]Wei Li, Pin-Yu Chen, Sijia Liu, Ren Wang:
PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection. CoRR abs/2406.05826 (2024) - [i145]Zonglin Di, Zhaowei Zhu, Jinghan Jia, Jiancheng Liu, Zafar Takhirov, Bo Jiang, Yuanshun Yao, Sijia Liu, Yang Liu:
Label Smoothing Improves Machine Unlearning. CoRR abs/2406.07698 (2024) - [i144]Jiabao Ji, Yujian Liu, Yang Zhang, Gaowen Liu, Ramana Rao Kompella, Sijia Liu, Shiyu Chang:
Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference. CoRR abs/2406.08607 (2024) - [i143]Hongkang Li, Meng Wang, Shuai Zhang, Sijia Liu, Pin-Yu Chen:
Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis. CoRR abs/2406.17167 (2024) - [i142]Yuguang Yao, Anil K. Jain, Sijia Liu:
Adversarial Watermarking for Face Recognition. CoRR abs/2409.16056 (2024) - [i141]Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng:
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints. CoRR abs/2410.06458 (2024) - [i140]Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu:
Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning. CoRR abs/2410.07163 (2024) - [i139]Jinghan Jia, Jiancheng Liu, Yihua Zhang, Parikshit Ram, Nathalie Baracaldo, Sijia Liu:
WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models. CoRR abs/2410.17509 (2024) - [i138]Haomin Zhuang, Yihua Zhang, Kehan Guo, Jinghan Jia, Gaowen Liu, Sijia Liu, Xiangliang Zhang:
UOE: Unlearning One Expert Is Enough For Mixture-of-experts LLMS. CoRR abs/2411.18797 (2024) - [i137]Changchang Sun, Ren Wang, Yihua Zhang, Jinghan Jia, Jiancheng Liu, Gaowen Liu, Sijia Liu, Yan Yan:
Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image Classification. CoRR abs/2412.16780 (2024) - 2023
- [j21]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. Proc. ACM Hum. Comput. Interact. 7(CSCW2): 1-32 (2023) - [c132]Sijia Liu, Patrick Lange, Behnam Hedayatnia, Alexandros Papangelis, Di Jin, Andrew Wirth, Yang Liu, Dilek Hakkani-Tur:
Towards Credible Human Evaluation of Open-Domain Dialog Systems Using Interactive Setup. AAAI 2023: 13264-13272 - [c131]Pin-Yu Chen, Sijia Liu:
Holistic Adversarial Robustness of Deep Learning Models. AAAI 2023: 15411-15420 - [c130]Sijia Liu:
AAAI New Faculty Highlights: General and Scalable Optimization for Robust AI. AAAI 2023: 15447 - [c129]Soumyadeep Pal, Ren Wang, Yuguang Yao, Sijia Liu:
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning. SafeAI@AAAI 2023 - [c128]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 - [c127]Sijia Liu, Jiahao Liu, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, Ning Gu:
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation. CIKM 2023: 1493-1502 - [c126]Ren Wang, Yuxuan Li, Sijia Liu:
Exploring Diversified Adversarial Robustness in Neural Networks via Robust Mode Connectivity. CVPR Workshops 2023: 2346-2352 - [c125]Haomin Zhuang, Yihua Zhang, Sijia Liu:
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion. CVPR Workshops 2023: 2385-2392 - [c124]Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding:
Text-Visual Prompting for Efficient 2D Temporal Video Grounding. CVPR 2023: 14794-14804 - [c123]Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang, Sijia Liu:
Understanding and Improving Visual Prompting: A Label-Mapping Perspective. CVPR 2023: 19133-19143 - [c122]Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, Dilek Hakkani-Tur:
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines. EMNLP (Findings) 2023: 14031-14047 - [c121]Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen, Sijia Liu:
Visual Prompting for Adversarial Robustness. ICASSP 2023: 1-5 - [c120]Jinghan Jia, Yihua Zhang, Dogyoon Song, Sijia Liu, Alfred O. Hero III:
Robustness-Preserving Lifelong Learning Via Dataset Condensation. ICASSP 2023: 1-5 - [c119]Hui Li, Jinghan Jia, Shijun Liang, Yuguang Yao, Saiprasad Ravishankar, Sijia Liu:
SMUG: Towards Robust Mri Reconstruction by Smoothed Unrolling. ICASSP 2023: 1-5 - [c118]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. ICCV 2023: 90-101 - [c117]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 - [c116]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 - [c115]Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen:
A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity. ICLR 2023 - [c114]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 - [c113]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 - [c112]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 - [c111]Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu:
Model Sparsity Can Simplify Machine Unlearning. NeurIPS 2023 - [c110]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. NeurIPS 2023 - [c109]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. NeurIPS 2023 - [c108]Sarik Ghazarian, Behnam Hedayatnia, Di Jin, Sijia Liu, Nanyun Peng, Yang Liu, Dilek Hakkani-Tur:
MERCY: Multiple Response Ranking Concurrently in Realistic Open-Domain Conversational Systems. SIGDIAL 2023: 615-631 - [c107]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 - [i136]Soumyadeep Pal, Ren Wang, Yuguang Yao, Sijia Liu:
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning. CoRR abs/2301.08751 (2023) - [i135]Alex Gu, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Certified Interpretability Robustness for Class Activation Mapping. CoRR abs/2301.11324 (2023) - [i134]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) - [i133]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) - [i132]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) - [i131]Jinghan Jia, Yihua Zhang, Dogyoon Song, Sijia Liu, Alfred O. Hero III:
Robustness-preserving Lifelong Learning via Dataset Condensation. CoRR abs/2303.04183 (2023) - [i130]Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding:
Text-Visual Prompting for Efficient 2D Temporal Video Grounding. CoRR abs/2303.04995 (2023) - [i129]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) - [i128]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) - [i127]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) - [i126]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) - [i125]Haomin Zhuang, Yihua Zhang, Sijia Liu:
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion. CoRR abs/2303.16378 (2023) - [i124]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) - [i123]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) - [i122]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) - [i121]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) - [i120]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) - [i119]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) - [i118]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) - [i117]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) - [i116]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) - [i115]Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Sijia Liu, Tsung-Yi Ho:
AutoVP: An Automated Visual Prompting Framework and Benchmark. CoRR abs/2310.08381 (2023) - [i114]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) - [i113]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) - [i112]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) - [i111]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) - [i110]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) - [i109]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) - [i108]Can Jin, Tianjin Huang, Yihua Zhang, Mykola Pechenizkiy, Sijia Liu, Shiwei Liu, Tianlong Chen:
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective. CoRR abs/2312.01397 (2023) - [i107]Xiao Guo, Vishal Asnani, Sijia Liu, Xiaoming Liu:
Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network. CoRR abs/2312.02224 (2023) - [i106]Shijun Liang, Van Hoang Minh Nguyen, Jinghan Jia, Ismail Alkhouri, Sijia Liu, Saiprasad Ravishankar:
Robust MRI Reconstruction by Smoothed Unrolling (SMUG). CoRR abs/2312.07784 (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) - [c106]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 - [c105]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Zeroth-Order Optimization for Composite Problems with Functional Constraints. AAAI 2022: 7453-7461 - [c104]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 - [c103]Vishal Asnani, Xi Yin, Tal Hassner, Sijia Liu, Xiaoming Liu:
Proactive Image Manipulation Detection. CVPR 2022: 15365-15374 - [c102]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 - [c101]Yifan Gong, Yuguang Yao, Yize Li, Yimeng Zhang, Xiaoming Liu, Xue Lin, Sijia Liu:
Reverse Engineering of Imperceptible Adversarial Image Perturbations. ICLR 2022 - [c100]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 - [c99]Tianshu Huang, Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang:
Optimizer Amalgamation. ICLR 2022 - [c98]