JSM2023
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Contributed Papers

Challenges in Data Science: Missing, Noisy and Imbalanced Data

Sun, Aug 6, 2:00 PM
Zhiwei ZhuChair
Section on Statistical Learning and Data Science

7 Presentations

Impossibility of Learning Bayes Classifier from Noisy Responses with Unkown Noise Rates
2:05 PM - 2:20 PM
Soham Bakshi (University of Michigan)
Co-authors: Subha Maity (University of Michigan)
Machine Learning for Evidence Building – Building Scientific Productivity Data through Linking
2:20 PM - 2:35 PM
Wan-Ying Chang (National Science Foundation)
Co-authors: Eric Livingston (Elsevier), Haoyi Wei (ORISE)
Data-Driven Learning for Imbalanced Data
2:35 PM - 2:50 PM
Elizabeth Chou (National Chengchi University)
Penalized Estimation of Covariance and Precision Matrices using Blockwise Missing Multimodal Data
2:50 PM - 3:05 PM
Joseph Graves
Co-authors: Yufeng Liu (University of North Carolina at Chapel Hill), Yongli Zhang (Lyft), Seong-Tae Kim (NC A&T State University)
Identifiability and estimation of exponential family models under missing not at random mechanism
3:05 PM - 3:20 PM
Anna Guo (Emory University, Rollins School of Public Health)
Co-authors: Jiwei Zhao (University of Wisconsin-Madison), Razieh Nabi (Emory University, Rollins School of Public Health)
WITHDRAWN Non-universal data: The impact of unobserved feature missingness on algorithmic fairness
3:20 PM - 3:35 PM
Co-authors: Nil-Jana Akpinar, Zachary Lipton (Carnegie Mellon University), Alexandra Chouldechova
Modeling missing at random multivariate discrete data using a mixture of binomial product experts
3:35 PM - 3:50 PM
Daniel Suen (University Of Washington)
Co-authors: Yen-Chi Chen

Session participants

Cheryl Brooks (AT&T Data Science and AI Research)
Participant
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