Sijia Liu

Mingyi Hong, Sijia Liu, Ioannis Tsaknakis, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

Yu Cheng, Shiyu Chang, Tianlong Chen, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations pdf

Parikshit Ram, Djallel Bouneffouf, Sijia Liu, 2020, IJCAI.

Learned Fine-Tuner for Incongruous Few-Shot Learning

Sijia Liu, Djallel Bouneffouf, P. Ram, 2020, ArXiv.

A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion

Sijia Liu, Yihua Zhang, Haomin Zhuang, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Defending against Backdoor Attack on Deep Neural Networks

Pin-Yu Chen, X. Lin, Sijia Liu, 2020, ArXiv.

Structured Adversarial Attack: Towards General Implementation and Better Interpretability pdf

Deniz Erdogmus, Yanzhi Wang, Sijia Liu, 2018, ICLR.

Reverse Engineering of Imperceptible Adversarial Image Perturbations

X. Lin, Sijia Liu, Yuguang Yao, 2022, ICLR.

When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? pdf

Chuang Gan, Gaoyuan Zhang, Lijie Fan, 2021, NeurIPS.

Fast Training of Provably Robust Neural Networks by SingleProp

Sijia Liu, Gaoyuan Zhang, Tsui-Wei Weng, 2021, AAAI.

Evading Real-Time Person Detectors by Adversarial T-shirt pdf

Quanfu Fan, Yanzhi Wang, Sijia Liu, 2019, ArXiv.

Interpreting Adversarial Examples by Activation Promotion and Suppression pdf

Chuang Gan, Quanfu Fan, Kaidi Xu, 2019, ArXiv.

Robust Overfitting may be mitigated by properly learned smoothening

Shiyu Chang, Zhangyang Wang, Sijia Liu, 2021, ICLR.

Adversarial T-Shirt! Evading Person Detectors in a Physical World

Quanfu Fan, Yanzhi Wang, Sijia Liu, 2019, ECCV.

Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature Extractors pdf

Sijia Liu, Ruixuan Wang, Changhong Zhong, 2021, ArXiv.

Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations

Pin-Yu Chen, Tsui-Wei Weng, Luca Daniel, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Higher-Order Certification for Randomized Smoothing pdf

Pin-Yu Chen, Sijia Liu, Tsui-Wei Weng, 2020, NeurIPS.

A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity

M. Wang, Sijia Liu, Pin-Yu Chen, 2023, ICLR.

Holistic Adversarial Robustness of Deep Learning Models

Pin-Yu Chen, Sijia Liu, 2022, AAAI.

Visual Prompting for Adversarial Robustness

Pin-Yu Chen, Sijia Liu, Yuguang Yao, 2022, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling

M. Wang, Pin-Yu Chen, Jinjun Xiong, 2022, ICML.

Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case pdf

Meng Wang, Sijia Liu, Shuai Zhang, 2020, ICML.

Towards Certificated Model Robustness Against Weight Perturbations

Pu Zhao, Luca Daniel, Pin-Yu Chen, 2020, AAAI.

Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM pdf

Yanzhi Wang, Jian Tang, Sijia Liu, 2019, ArXiv.

Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning

Chia-Mu Yu, Songtao Lu, Chia-Yi Hsu, 2021, AAAI.

A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM pdf

Jiayu Li, Jiaming Xie, Yun Liang, 2018, ArXiv.

Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

M. Wang, Sijia Liu, Pin-Yu Chen, 2023, ICML.

Adversarial Attack Generation Empowered by Min-Max Optimization

M. Fardad, Pin-Yu Chen, Jingkang Wang, 2019, NeurIPS.

Improved Linear Convergence of Training CNNs With Generalizability Guarantees: A One-Hidden-Layer Case

Jinjun Xiong, Meng Wang, Pin-Yu Chen, 2020, IEEE Transactions on Neural Networks and Learning Systems.

A Compression-Compilation Framework for On-mobile Real-time BERT Applications

Pu Zhao, Sijia Liu, Caiwen Ding, 2021, IJCAI.

Hidden Cost of Randomized Smoothing

Sijia Liu, Ching-Yun Ko, Pin-Yu Chen, 2021, AISTATS.

CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks

Sijia Liu, Tsui-Wei Weng, Pin-Yu Chen, 2018, AAAI.

SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency pdf

Yifan Gong, Yanzhi Wang, Xiaolong Ma, 2020, ArXiv.

Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

Sijia Liu, Liang Zhao, Tong Sun, 2023, Proc. ACM Hum. Comput. Interact..

On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization

Ruoyu Sun, Xiangyi Chen, Mingyi Hong, 2018, ICLR.

Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning

Pin-Yu Chen, Sijia Liu, Pin-Yu Chen, 2019, KDD.

NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

Yanzhi Wang, Sijia Liu, Geng Yuan, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

6.7ms on Mobile with over 78% ImageNet Accuracy: Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration pdf

Zhiyu Chen, Yanzhi Wang, Pu Zhao, 2020, ArXiv.

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Pin-Yu Chen, Zhangyang Wang, Sijia Liu, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Zeroth-Order Optimization for Composite Problems with Functional Constraints

Songtao Lu, Sijia Liu, Pin-Yu Chen, 2021, AAAI.

Dynamic Network Analysis of the 4D Nucleome

Alfred O. Hero, Sijia Liu, Pin-Yu Chen, 2018, bioRxiv.

On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method

Sijia Liu, Kaidi Xu, Pu Zhao, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Can 3D Adversarial Logos Cloak Humans? pdf

Yi Wang, Shiyu Chang, Tianlong Chen, 2020, ArXiv.

Proper Network Interpretability Helps Adversarial Robustness in Classification

Pin-Yu Chen, Shiyu Chang, Sijia Liu, 2020, ICML.

Progressive Weight Pruning of Deep Neural Networks using ADMM

M. Fardad, Yanzhi Wang, X. Lin, 2018, ArXiv.

Generation of Low Distortion Adversarial Attacks via Convex Programming

Yanzhi Wang, Sijia Liu, Makan Fardad, 2019, 2019 IEEE International Conference on Data Mining (ICDM).

Beyond Adversarial Training: Min-Max Optimization in Adversarial Attack and Defense pdf

Bo Li, Sijia Liu, Pin-Yu Chen, 2019, ArXiv.

An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks

Yanzhi Wang, Pu Zhao, Xue Lin, 2018, ACM Multimedia.

Understanding and Improving Visual Prompting: A Label-Mapping Perspective

Pin-Yu Chen, Sijia Liu, Yuguang Yao, 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness

Pin-Yu Chen, Zhangyang Wang, Sijia Liu, 2022, ICML.

Second Rethinking of Network Pruning in the Adversarial Setting

Yanzhi Wang, X. Lin, Sijia Liu, 2019, ArXiv.

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework

Lucani E. Daniel, Pin-Yu Chen, Sijia Liu, 2021, ICML.

A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity

M. Wang, Sijia Liu, Pin-Yu Chen, 2023, ICLR.

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing

Kush R. Varshney, Dennis Wei, Hazar Yueksel, 2020, ICML.

Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration pdf

Wei Niu, Yanzhi Wang, Zheng Zhan, 2021, ACM Trans. Design Autom. Electr. Syst..