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IMDS Journal Club: Confident Learning: Estimating Uncertainty in Dataset Labels
Yelena Bagdasarova (Phd, UW) discusses the article Confident Learning: Estimating Uncertainty in Dataset Labels, written by Curtis G. Northcutt, Lu Jiang, and Isaac L. Chuang.
Abstract: Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is a data-centric approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. Whereas numerous studies have developed these principles independently, here, we combine them, building on the assumption of a class-conditional noise process to directly estimate the joint distribution between noisy (given) labels and uncorrupted (unknown) labels. This results in a generalized CL which is provably consistent and experimentally performant. We present sufficient conditions where CL exactly fin…
Event interval: Single day event. Online Meeting Link: https://washington.zoom.us/j/92158637394?pwd=pi87aK9LVz5Jx7KTPgVq0SX7d2xNIL.1. Campus room: F107, 750 Republican St. Seattle 98109. Accessibility Contact: imds@uw.edu. Event Types: Academics. Lectures/Seminars. Target Audience: Data Science and Medical Researchers.
Monday, July 14, 2025, 1:00 PM – 2:00 PM.