Sijia Liu
Cited by
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Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
CC Tu, P Ting, PY Chen, S Liu, H Zhang, J Yi, CJ Hsieh, SM Cheng
Proceedings of the AAAI conference on artificial intelligence 33 (01), 742-749, 2019
Topology attack and defense for graph neural networks: An optimization perspective
K Xu, H Chen, S Liu, PY Chen, TW Weng, M Hong, X Lin
IJCAI 2019, 2019
Adversarial t-shirt! evading person detectors in a physical world
K Xu, G Zhang, S Liu, Q Fan, M Sun, H Chen, PY Chen, Y Wang, X Lin
ECCV 2020, 665-681, 2020
The lottery ticket hypothesis for pre-trained bert networks
T Chen, J Frankle, S Chang, S Liu, Y Zhang, Z Wang, M Carbin
Advances in neural information processing systems 33, 15834-15846, 2020
On the convergence of a class of adam-type algorithms for non-convex optimization
X Chen, S Liu, R Sun, M Hong
ICLR 2019, 2018
Adversarial robustness: From self-supervised pre-training to fine-tuning
T Chen, S Liu, S Chang, Y Cheng, L Amini, Z Wang
CVPR, 699-708, 2020
Sign-opt: A query-efficient hard-label adversarial attack
M Cheng, S Singh, P Chen, PY Chen, S Liu, CJ Hsieh
ICLR 2020, 2019
Sensor selection for estimation with correlated measurement noise
S Liu, SP Chepuri, M Fardad, E Maşazade, G Leus, PK Varshney
IEEE Transactions on Signal Processing 64 (13), 3509-3522, 2016
Robust overfitting may be mitigated by properly learned smoothening
T Chen, Z Zhang, S Liu, S Chang, Z Wang
International Conference on Learning Representations, 2020
A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications
S Liu, PY Chen, B Kailkhura, G Zhang, AO Hero III, PK Varshney
IEEE Signal Processing Magazine 37 (5), 43-54, 2020
Adversarial robustness vs. model compression, or both?
S Ye, K Xu, S Liu, H Cheng, JH Lambrechts, H Zhang, A Zhou, K Ma, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision, 111-120, 2019
Structured adversarial attack: Towards general implementation and better interpretability
K Xu, S Liu, P Zhao, PY Chen, H Zhang, Q Fan, D Erdogmus, Y Wang, ...
ICLR 2019, 2018
Cnn-cert: An efficient framework for certifying robustness of convolutional neural networks
A Boopathy, TW Weng, PY Chen, S Liu, L Daniel
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3240-3247, 2019
Zeroth-order stochastic variance reduction for nonconvex optimization
S Liu, B Kailkhura, PY Chen, P Ting, S Chang, L Amini
Advances in Neural Information Processing Systems 31, 2018
Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing
S Dutta, D Wei, H Yueksel, PY Chen, S Liu, K Varshney
International conference on machine learning, 2803-2813, 2020
Learning sparse graphs under smoothness prior
SP Chepuri, S Liu, G Leus, AO Hero
ICASSP 2017, 6508-6512, 2017
Practical detection of trojan neural networks: Data-limited and data-free cases
R Wang, G Zhang, S Liu, PY Chen, J Xiong, M Wang
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models
T Chen, J Frankle, S Chang, S Liu, Y Zhang, M Carbin, Z Wang
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
When does contrastive learning preserve adversarial robustness from pretraining to finetuning?
L Fan, S Liu, PY Chen, G Zhang, C Gan
Advances in neural information processing systems 34, 21480-21492, 2021
Zo-adamm: Zeroth-order adaptive momentum method for black-box optimization
X Chen, S Liu, K Xu, X Li, X Lin, M Hong, D Cox
Advances in neural information processing systems 32, 2019
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