Kevin Labille
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Researcher
Also affiliated: National Taiwan University of Science and Technology (2012)
Faculty Researcher
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Kevin Labille's research focuses on machine learning and artificial intelligence, particularly in areas related to natural language processing, fairness in algorithms, and predictive modeling. His work has explored the use of lexicon-based sentiment analysis for stock market prediction and the detection of fake news through emotion analysis. Labille has also investigated fairness considerations in contextual bandit algorithms, aiming to achieve user-side fairness and develop fairness-aware recommendation systems. His research extends to transfer learning for contextual bandits, utilizing prior observations to improve model performance.
Labille has published 17 papers and has an h-index of 5 with 142 citations. He has collaborated with several researchers at the University of Arkansas at Fayetteville, including Xintao Wu, Wen Huang, and Susan Gauch, with whom he shares multiple publications. His recent activity indicates ongoing research in these areas.
Metrics
- h-index: 5
- Publications: 17
- Citations: 144
Selected Publications
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Achieving User-Side Fairness in Contextual Bandits (2022)
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Fairness-aware Bandit-based Recommendation (2021)
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Transferable Contextual Bandits with Prior Observations (2021)
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Lexicon-based sentiment analysis for stock movement prediction (2021)
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Semantic Distance Spreading Across Entities in Linked Open Data (2019)
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Text Mining for Word Sentiment Detection (2018)
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Employing Link Differentiation in Linked Data Semantic Distance (2017)
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PLDSD: Propagated Linked Data Semantic Distance (2017)
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Estimating Sentiment via Probability and Information Theory (2016)
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Conceptual Impact-Based Recommender System for CiteSeerx. (2015)
Collaboration Network
Top Collaborators
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Transferable Contextual Bandits with Prior Observations
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Lexicon-based sentiment analysis for stock movement prediction
- Lexicon-based sentiment analysis for stock movement prediction
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