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researcher

Lina Yao

6 papers hereh-index 251.9k citations83 works total

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

author position
  • middle author4
  • last author2

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.DL1
  • cs.IR1
  • cs.NI1
same name
  • Lina Yao — 54 papers, h 50
  • Lina Yao — 20 papers, h 10
  • Lina Yao — 16 papers
  • Lina Yao — 15 papers, h 3
  • Lina Yao — 14 papers, h 7
  • Lina Yao — 12 papers, 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
20152023
most citedUnsupervised Feature Analysis with Class Margin Optimization

13 citations · 24 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023

Reinforcement Learning for Generative AI: A Survey

Yuanjiang Cao, Quan Z. Sheng, Julian McAuley +1

Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer visi…

cs.LG2020★ 3 cited

Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems

Dai Hoang Tran, Quan Z. Sheng, Wei Emma Zhang +5

In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applicatio…

cs.LG2016★ 2 cited

Uncovering Locally Discriminative Structure for Feature Analysis

Sen Wang, Feiping Nie, Xiaojun Chang +3

Manifold structure learning is often used to exploit geometric information among data in semi-supervised feature learning algorithms. In this paper, we find that local discriminati…

cs.LG2015★ 13 cited

Unsupervised Feature Analysis with Class Margin Optimization

Sen Wang, Feiping Nie, Xiaojun Chang +3

Unsupervised feature selection has been always attracting research attention in the communities of machine learning and data mining for decades. In this paper, we propose an unsupe…

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