Event Date
Speaker: Yuval Benjamini (Statistics and Data Science, The Hebrew University of Jerusalem, Israel
Title: "A random-class model for evaluating highly multi-class classification tasks"
Abstract: In many multi-class classification tasks, the potential set of classes is vast (think face detection or speaker identification). Researchers often design and evaluate the classifiers on a subset from the population of classes to which they will be applied. In this talk, I argue that it may be useful to model the observed set of classes as a random sample from a population. As the main results, I will present parametric and nonparametric characterizations for the effect of the number of classes on classification accuracy in a few-shot learning design. For the non-parametric model, which empirically better describes real data-sets, we develop practical methods for estimation with relatively few observed classes. This allows us to predict the classification accuracy as more classes are added. I also discuss use-cases from neuroscience use-cases including evaluating representations in brain-decoding tasks and subject privacy.
The talk is based on work with Yuli Slavutsky and Charles Zheng.
Faculty webpage: https://en.stat.huji.ac.il/people/yuval-binyamini (links to Hebrew University of Jerusalem)
Seminar Date/Time: Tuesday, April 18, 2023 at 4:10pm
Location: MSB 1147 (Colloquium Room).
Refreshments: 3:30pm, MSB 1147