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researcher

Kai Liu

15 papers hereh-index 5717.2k citations1k works total

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

author position
  • first author3
  • middle author6
  • last author5

Across the 14 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.CL2
  • math.OC2
  • cond-mat.mtrl-sci1
  • cs.AI1
  • cs.CR1
same name
  • Kai Liu — 42 papers, h 25
  • Kai Liu — 33 papers, h 45
  • Kai Liu — 21 papers, h 5
  • Kai Liu — 15 papers, h 24
  • Kai Liu — 14 papers
  • Kai Liu — 12 papers, h 7

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
20172023
most citedA review of knowledge graph application scenarios in cyber security

18 citations · 23 across the 8 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023

On Regularized Sparse Logistic Regression

Mengyuan Zhang, Kai Liu

Sparse logistic regression is for classification and feature selection simultaneously. Although many studies have been done to solve ℓ1​-regularized logistic regression, there…

cs.LG2023

Adaptive Weighted Multiview Kernel Matrix Factorization with its application in Alzheimer's Disease Analysis -- A clustering Perspective

Kai Liu, Yarui Cao

Recent technology and equipment advancements provide with us opportunities to better analyze Alzheimer's disease (AD), where we could collect and employ the data from different ima…

cs.LG2019★ 1 cited

Incentivized Exploration for Multi-Armed Bandits under Reward Drift

Zhiyuan Liu, Huazheng Wang, Fan Shen +2

We study incentivized exploration for the multi-armed bandit (MAB) problem where the players receive compensation for exploring arms other than the greedy choice and may provide bi…

cs.LG2019★ 1 cited

Spherical Principal Component Analysis

Kai Liu, Qiuwei Li, Hua Wang +1

Principal Component Analysis (PCA) is one of the most important methods to handle high dimensional data. However, most of the studies on PCA aim to minimize the loss after projecti…

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