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researcher

Jiuyong Li

Adelaide University

42 papers hereh-index 498.7k citations328 works total

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

author position
  • first author7
  • middle author25
  • last author5

Across the 37 of 42 papers where every author was matched, so the position is known.

fields
  • cs.LG18
  • cs.AI14
  • stat.ME5
  • cs.CY2
  • cs.DB2
  • q-bio.GN1
affiliations
  • Adelaide University
Homepage
same name
  • Jiuyong Li — 30 papers, h 10
  • Jiuyong Li — 1 paper
  • Jiuyong Li — 1 paper, h 11

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 citedA fast PC algorithm for high dimensional causal discovery with multi-core PCs

144 citations · 407 across the 41 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 28 cited

Causality-based Feature Selection: Methods and Evaluations

Kui Yu, Xianjie Guo, Lin Liu +4

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between p…

cs.DB2019★ 1 cited

Linking Graph Entities with Multiplicity and Provenance

Jixue Liu, Selasi Kwashie, Jiuyong Li +2

Entity linking and resolution is a fundamental database problem with applications in data integration, data cleansing, information retrieval, knowledge fusion, and knowledge-base p…

stat.ME2019★ 1 cited

Identify treatment effect patterns for personalised decisions

Jiuyong Li, Lin Liu, Shisheng Zhang +3

In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an individual. Such evidence can be obtained by modelling treatment…

cs.LG2019★ 9 cited

Robust Multi-instance Learning with Stable Instances

Weijia Zhang, Jiuyong Li, Lin Liu

Multi-instance learning (MIL) deals with tasks where data is represented by a set of bags and each bag is described by a set of instances. Unlike standard supervised learning, only…

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