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cs.LG2026
Measuring the Prevalence of Policy Violating Content with ML Assisted Sampling and LLM Labeling
Attila Dobi, Aravindh Manickavasagam, Benjamin Thompson +2
Content safety teams need metrics that reflect what users actually experience, not only what is reported. We study prevalence: the fraction of user views (impressions) that went to…
cs.LG2017
A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure
Prithwish Chakraborty, Vishrawas Gopalakrishnan, Sharon M. H. Alford +1
Electronic medical records (EMR) contain longitudinal information about patients that can be used to analyze outcomes. Typically, studies on EMR data have worked with established v…