POST-HOC LOSS-CALIBRATION FOR BAYESIAN NEURAL NETWORKS

von Vadera, Meet P.
Veröffentlicht in Conference on Uncertainty in Artificial Intelligence (37. : 2021 : Online) 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021) ; Part 2 of 3 (2022)

STATE OF THE ART CAUSAL INFERENCE IN THE PRESENCE OF EXTRANEOUS COVARIATES: A SIMULATION STUDY

von Cobzaru, Raluca
Veröffentlicht in AMIA Annual Symposium (2021 : San Diego, Calif.; Online) American Medical Informatics Association Annual Symposium (AMIA 2021) ; Volume 1 of 3 (2022)

PREDICTING TYPE 1 DIABETES ONSET USING NOVEL SURVIVAL ANALYSIS WITH BIOMARKER ONTOLOGY

von Li, Ying
Veröffentlicht in American Medical Informatics Association (2020 : Online) American Medical Informatics Association Annual Symposium (AMIA 2020) ; Volume 2 of 3 (2021)

Embracing Disease Progression with a Learning System for Real World Evidence Discovery

von Tang, Zefang
Veröffentlicht in ICIC (16. : 2020 : Bari) 16th International Conference, ICIC 2020, Bari, Italy, October 2-5, 2020 ; part 2: Intelligent computing theories and application (2020)

MODELING DISEASE PROGRESSION TRAJECTORIES FROM LONGITUDINAL OBSERVATIONAL DATA

von Kwon, Bum Chul
Veröffentlicht in American Medical Informatics Association (2020 : Online) American Medical Informatics Association Annual Symposium (AMIA 2020) ; Volume 2 of 3 (2021)

IMPACT OF CLINICAL AND GENOMIC FACTORS ON COVID-19 DISEASE SEVERITY

von Dey, Sanjoy
Veröffentlicht in AMIA Annual Symposium (2021 : San Diego, Calif.; Online) American Medical Informatics Association Annual Symposium (AMIA 2021) ; Volume 1 of 3 (2022)