ติดตาม
Lili Su
Lili Su
Assistant Professor, Northeastern University
ยืนยันอีเมลแล้วที่ northeastern.edu - หน้าแรก
ชื่อ
อ้างโดย
อ้างโดย
ปี
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
7882017
Byzantine Multi-Agent Optimization: Part II
L Su, N Vaidya
arXiv preprint arXiv:1507.01845, 2015
202*2015
Securing distributed machine learning in high dimensions
L Su, J Xu
SIGMETRICS 2019 arXiv preprint arXiv:1804.10140, 2018
116*2018
Byzantine-resilient multiagent optimization
L Su, NH Vaidya
IEEE Transactions on Automatic Control 66 (5), 2227-2233, 2020
872020
Finite-time guarantees for Byzantine-resilient distributed state estimation with noisy measurements
L Su, S Shahrampour
Transactions on Automatic Control (TAC), 2020
762020
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective
L Su, P Yang
NeurIPS2019, 2019
602019
Defending Non-Bayesian Learning against Adversarial Attacks
L Su, NH Vaidya
Distributed Computing arXiv: 1606.08883, 2016
582016
Reaching approximate Byzantine consensus with multi-hop communication
L Su, NH Vaidya
Information and Computation 255, 352-368, 2017
512017
Non-bayesian learning in the presence of byzantine agents
L Su, NH Vaidya
Distributed Computing: 30th International Symposium, DISC 2016, Paris …, 2016
412016
Multi-agent optimization in the presence of Byzantine adversaries: Fundamental limits
LSN Vaidya
2016 American Control Conference (ACC), 7183-7188, 2016
402016
Fault-tolerant distributed optimization (Part IV): Constrained optimization with arbitrary directed networks
L Su, NH Vaidya
arXiv preprint arXiv:1511.01821, 2015
312015
Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits
L Su, CJ Chang, N Lynch
Neural Computation 31, 2523-2561, 2019
242019
Asynchronous distributed hypothesis testing in the presence of crash failures
L Su, NH Vaidya
arXiv preprint arXiv:1606.03418, 2016
192016
Defending distributed systems against adversarial attacks: consensus, consensusbased learning, and statistical learning
L Su
ACM SIGMETRICS Performance Evaluation Review 47 (3), 24-27, 2020
162020
Experimental design networks: A paradigm for serving heterogeneous learners under networking constraints
Y Li, Y Liu, L Su, E Yeh, S Ioannidis
IEEE/ACM Transactions on Networking 31 (5), 2236-2250, 2023
142023
Robust multi-agent optimization: coping with byzantine agents with input redundancy
L Su, NH Vaidya
International Symposium on Stabilization, Safety, and Security of …, 2016
142016
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
132019
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points
L Su, J Xu, P Yang
Accepted to Journal of Machine Learning Research (JMLR), 2021
112021
Distributed Learning with Adversarial Agents Under Relaxed Network Condition
P Vyavahare, L Su, NH Vaidya
Fusion 2019, 2019
102019
Distributed learning over time-varying graphs with adversarial agents
P Vyavahare, L Su, NH Vaidya
2019 22th International Conference on Information Fusion (FUSION), 1-8, 2019
92019
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บทความ 1–20