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researcher

Lili Su

28 papers hereh-index 181.9k citations46 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author18
  • middle author3
  • last author6

Across the 27 of 28 papers where every author was matched, so the position is known.

fields
  • cs.DC13
  • cs.LG9
  • cs.DS1
  • cs.IT1
  • cs.NI1
  • eess.SY1
same name
  • Lili Su — 6 papers, h 4
  • Lili Su — 5 papers, h 2
  • Lili Su — 5 papers, h 2
  • Lili Su — 4 papers, h 2
  • Lili Su — 4 papers, h 3
  • Lili Su — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132023
most citedDistributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent

140 citations · 293 across the 25 of their papers we have counts for

collaborators
Showing 2015Show all

4 papers · 1 filter

cs.DC2015★ 18 cited

Fault-Tolerant Distributed Optimization (Part IV): Constrained Optimization with Arbitrary Directed Networks

Lili Su, Nitin H. Vaidya

We study the problem of constrained distributed optimization in multi-agent networks when some of the computing agents may be faulty. In this problem, the system goal is to have al…

cs.DC2015★ 8 cited

Fault-Tolerant Multi-Agent Optimization: Part III

Lili Su, Nitin Vaidya

We study fault-tolerant distributed optimization of a sum of convex (cost) functions with real-valued scalar input/output in the presence of crash faults or Byzantine faults. In pa…

cs.DC2015★ 19 cited

Byzantine Multi-Agent Optimization: Part I

Lili Su, Nitin Vaidya

We study Byzantine fault-tolerant distributed optimization of a sum of convex (cost) functions with real-valued scalar input/ouput. In particular, the goal is to optimize a global…

cs.DC2015★ 33 cited

Byzantine Multi-Agent Optimization: Part II

Lili Su, Nitin Vaidya

In Part I of this report, we introduced a Byzantine fault-tolerant distributed optimization problem whose goal is to optimize a sum of convex (cost) functions with real-valued scal…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.