Giri Gopalan

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks

This work demonstrates how statistical methodology can be used for quantifying uncertainty in metrics that have been aggregated across multiple tasks, and emphasizes bootstrapping, Bayesian hierarchical (i.e., multilevel) modeling, and the visualization of task weightings that consider standard errors.