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researcher

Cong Xie

University of Illinois Urbana-Champaign

16 papers hereh-index 162.5k citations26 works total

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

author position
  • sole author1
  • first author11
  • middle author4

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.DC5
  • cs.CR1
  • cs.SI1
  • math.NA1
affiliations
  • University of Illinois Urbana-Champaign
Homepage
same name
  • Cong Xie — 7 papers, h 5
  • Cong Xie — 4 papers, h 3
  • Cong Xie — 3 papers, h 2
  • Cong Xie — 2 papers, h 6
  • Cong Xie — 1 paper
  • Cong Xie — 1 paper, h 1

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
20152023
most citedAsynchronous Federated Optimization

434 citations · 840 across the 16 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.DC2018★ 44 cited

Phocas: dimensional Byzantine-resilient stochastic gradient descent

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta

We propose a novel robust aggregation rule for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily mani…

cs.LG2018★ 83 cited

Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta

We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers. Zeno generaliz…

cs.DC2018★ 173 cited

Generalized Byzantine-tolerant SGD

Cong Xie, Oluwasanmi Koyejo, Indranil Gupta

We propose three new robust aggregation rules for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily m…

cs.LG2018

Attack RMSE Leaderboard: An Introduction and Case Study

Cong Xie

In this manuscript, we briefly introduce several tricks to climb the leaderboards which use RMSE for evaluation without exploiting any training data.

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