Sijia Liu
Sijia Liu
Assistant Professor, Michigan State University; Affiliate Professor, MIT-IBM Watson AI Lab
Verified email at - Homepage
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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
On the convergence of a class of adam-type algorithms for non-convex optimization
X Chen, S Liu, R Sun, M Hong
arXiv preprint arXiv:1808.02941, 2018
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
arXiv preprint arXiv:1906.04214, 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
Learning sparse graphs under smoothness prior
SP Chepuri, S Liu, G Leus, AO Hero
2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017
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, ...
arXiv preprint arXiv:1808.01664, 2018
Optimal periodic sensor scheduling in networks of dynamical systems
S Liu, M Fardad, E Masazade, PK Varshney
IEEE Transactions on Signal Processing 62 (12), 3055-3068, 2014
Zeroth-order stochastic variance reduction for nonconvex optimization
S Liu, B Kailkhura, PY Chen, P Ting, S Chang, L Amini
arXiv preprint arXiv:1805.10367, 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
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
European Conference on Computer Vision, 665-681, 2020
Adversarial robustness: From self-supervised pre-training to fine-tuning
T Chen, S Liu, S Chang, Y Cheng, L Amini, Z Wang
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
The lottery ticket hypothesis for pre-trained bert networks
T Chen, J Frankle, S Chang, S Liu, Y Zhang, Z Wang, M Carbin
arXiv preprint arXiv:2007.12223, 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
Zeroth-order online alternating direction method of multipliers: Convergence analysis and applications
S Liu, J Chen, PY Chen, A Hero
International Conference on Artificial Intelligence and Statistics, 288-297, 2018
Sign-opt: A query-efficient hard-label adversarial attack
M Cheng, S Singh, P Chen, PY Chen, S Liu, CJ Hsieh
arXiv preprint arXiv:1909.10773, 2019
Sparsity-aware sensor collaboration for linear coherent estimation
S Liu, S Kar, M Fardad, PK Varshney
IEEE Transactions on Signal Processing 63 (10), 2582-2596, 2015
Automated machine learning via ADMM
S Liu, P Ram, D Bouneffouf, G Bramble, AR Conn, H Samulowitz, ...
CoRR, vol. abs/1905.00424, 2019
On the design of black-box adversarial examples by leveraging gradient-free optimization and operator splitting method
P Zhao, S Liu, PY Chen, N Hoang, K Xu, B Kailkhura, X Lin
Proceedings of the IEEE/CVF International Conference on Computer Vision, 121-130, 2019
A unified framework of dnn weight pruning and weight clustering/quantization using admm
S Ye, T Zhang, K Zhang, J Li, J Xie, Y Liang, S Liu, X Lin, Y Wang
arXiv preprint arXiv:1811.01907, 2018
Progressive dnn compression: A key to achieve ultra-high weight pruning and quantization rates using admm
S Ye, X Feng, T Zhang, X Ma, S Lin, Z Li, K Xu, W Wen, S Liu, J Tang, ...
arXiv preprint arXiv:1903.09769, 2019
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