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Feng Qi

9 papers hereh-index 486 citations9 works total

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

author position
  • sole author1
  • first author3
  • middle author5

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AI1
  • cs.CL1
  • cs.CV1
  • cs.DC1
  • q-bio.NC1
same name
  • Feng Qi — 4 papers
  • Feng Qi — 4 papers, h 3
  • Feng Qi — 4 papers, h 3
  • Feng Qi — 2 papers, h 3
  • Feng Qi — 2 papers, h 1
  • Feng Qi — 1 paper, h 4

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
20192023
most citedA Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs

16 citations · 26 across the 9 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 2 cited

Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation

Hanyang Liu, Shuai Yang, Feng Qi +1

Recent automated machine learning systems often use learning curves ranking models to inform decisions about when to stop unpromising trials and identify better model configuration…

cs.LG2021★ 1 cited

A framework for massive scale personalized promotion

Yitao Shen, Yue Wang, Xingyu Lu +6

Technology companies building consumer-facing platforms may have access to massive-scale user population. In recent years, promotion with quantifiable incentive has become a popula…

cs.LG2021★ 16 cited

A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs

Runzhong Wang, Zhigang Hua, Gan Liu +6

Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuris…

cs.LG2021★ 1 cited

Learning to Schedule DAG Tasks

Zhigang Hua, Feng Qi, Gan Liu +1

Scheduling computational tasks represented by directed acyclic graphs (DAGs) is challenging because of its complexity. Conventional scheduling algorithms rely heavily on simple heu…

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