Publications

[7] Y Li, M De-Arteaga, M Saar-Tsechansky, “Label Bias: A Pervasive and Invisibalized Problem” The Notices of American Mathematical Society (AMS), Accepted

[6] Y Li, M De-Arteaga, M Saar-Tsechansky, “Mitigating Label Bias via Decoupled Confident Learning” ICML 2023 AI&HCI workshop

[5] S Srivastava, Z Xu, Y Li, N Street, S Gilbertson-White, “Gaussian Process Regression and Classification using International Classification of Disease Codes as Covariates” Stat, 2023 Link

[4] Y Li, M De-Arteaga, M Saar-Tsechansky, “When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias”, the AAAI Conference on Human Computation and Crowdsourcing, 2022, Link

[3] S White-Gilbertson, S Srivastava, Y Li, E Laures, S Saeidzadeh, C Yeung, S Chae, “Multimorbidity and cancer: using electronic health record (EHR) data to cluster patients in multimorbidity phenotypes” Journal of Pain and Symptom Management (JPSM), 2019

[2] Y Wang, A Wang, Z Liu, A Thurman, L Powers, M Zou, A Hefel, Y Li, J Zabner, K.F. Au,”Single-molecule Long-read Sequencing Reveals the Chromatin Basis of Gene Expression” Genome Research, 2019. Link

[1] Y Li, T Wang, “Next Hit Predictor-Self-exciting Risk Modeling for Predicting Next Locations of Serial Crimes”, AI for Social Good Workshop NeurIPS 2018. Link

Work in progress

[1] T Wang, J Yang, Y Li, B Wang, “Partially Interpretable Estimators (PIE): Black-box-refined Interpretable Machine Learning” Link Minor Revision at Informs Journal of Computing