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Lili Su
Lili Su
Assistant Professor, Northeastern University
Verified email at northeastern.edu - Homepage
Title
Cited by
Cited by
Year
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Y Chen, L Su, J Xu
Proceedings of the ACM on Measurement and Analysis of Computing Systems 1 (2 …, 2017
3892017
Fault-tolerant multi-agent optimization: optimal iterative distributed algorithms
L Su, NH Vaidya
Proceedings of the 2016 ACM symposium on principles of distributed computing …, 2016
1152016
Securing distributed machine learning in high dimensions
L Su, J Xu
SIGMETRICS 2019 arXiv preprint arXiv:1804.10140, 2018
57*2018
Defending Non-Bayesian Learning against Adversarial Attacks
L Su, NH Vaidya
Distributed Computing arXiv: 1606.08883, 2016
452016
Byzantine Multi-Agent Optimization: Part II
L Su, N Vaidya
arXiv preprint arXiv:1507.01845, 2015
392015
Byzantine multi-agent optimization: Part I
L Su, N Vaidya
arXiv preprint arXiv:1506.04681, 2015
392015
Finite-time guarantees for Byzantine-resilient distributed state estimation with noisy measurements
L Su, S Shahrampour
Transactions on Automatic Control (TAC), 2020
362020
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective
L Su, P Yang
NeurIPS2019, 2019
352019
Non-bayesian learning in the presence of byzantine agents
L Su, NH Vaidya
International symposium on distributed computing, 414-427, 2016
342016
Reaching approximate byzantine consensus with multi-hop communication
L Su, NH Vaidya
Information and Computation 255, 352-368, 2017
332017
Byzantine-resilient multiagent optimization
L Su, NH Vaidya
IEEE Transactions on Automatic Control 66 (5), 2227-2233, 2020
272020
Fault-tolerant distributed optimization (Part IV): Constrained optimization with arbitrary directed networks
L Su, NH Vaidya
arXiv preprint arXiv:1511.01821, 2015
232015
Multi-agent optimization in the presence of Byzantine adversaries: Fundamental limits
LSN Vaidya
2016 American Control Conference (ACC), 7183-7188, 2016
212016
Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits
L Su, CJ Chang, N Lynch
Neural Computation 31, 2523-2561, 2019
172019
Asynchronous distributed hypothesis testing in the presence of crash failures
L Su, NH Vaidya
arXiv preprint arXiv:1606.03418, 2016
152016
Fault-tolerant multi-agent optimization: Part iii
L Su, N Vaidya
arXiv preprint arXiv:1509.01864, 2015
15*2015
Defending distributed systems against adversarial attacks: consensus, consensusbased learning, and statistical learning
L Su
ACM SIGMETRICS Performance Evaluation Review 47 (3), 24-27, 2020
142020
Robust multi-agent optimization: coping with byzantine agents with input redundancy
L Su, NH Vaidya
International Symposium on Stabilization, Safety, and Security of …, 2016
92016
Distributed Learning with Adversarial Agents Under Relaxed Network Condition
P Vyavahare, L Su, NH Vaidya
Fusion 2019, 2019
82019
Collaboratively learning the best option on graphs, using bounded local memory
L Su, M Zubeldia, N Lynch
Proceedings of the ACM on Measurement and Analysis of Computing Systems 3 (1 …, 2019
72019
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