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stat.ML2025
Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Rachel Longjohn, Giri Gopalan, Emily Casleton
Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus and then adapted to solve a variety of…
stat.ML2015★ 1 cited
Quantification of observed prior and likelihood information in parametric Bayesian modeling
Giri Gopalan
Two data-dependent information metrics are developed to quantify the information of the prior and likelihood functions within a parametric Bayesian model, one of which is closely r…
stat.ML2015
Admissibility of a posterior predictive decision rule
Giri Gopalan
Recent decades have seen an interest in prediction problems for which Bayesian methodology has been used ubiquitously. Sampling from or approximating the posterior predictive distr…