Kevin Labille Source Confirmed

OverviewAI-generated summary

Kevin Labille's research focuses on the application of machine learning techniques to problems in areas such as finance, fairness in algorithms, and information detection. He has published work investigating lexicon-based sentiment analysis for stock market prediction, as well as methods for achieving user-side fairness in contextual bandit algorithms and recommendation systems. Additional publications explore the detection of fake news through emotion analysis and the optimization of statistical distance measures in multivariate Support Vector Machines (SVM) for sentiment quantification. Labille also studies transferable contextual bandits incorporating prior observations.

His scholarly contributions include 17 publications, which have garnered 142 citations, resulting in an h-index of 5. He has collaborated with several researchers at the University of Arkansas at Fayetteville, including Xintao Wu, Wen Huang, and Susan Gauch, each with whom he shares three co-authored publications.

Metrics

• h-index: 5
• Publications: 17
• Citations: 142