# Bayesian Inference for Correlated Human Experts and Classifiers Source: [Bayesian Inference for Correlated Human Experts and Classifiers]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:53:53.795Z Authors: Markelle Kelly, Alex Boyd, Sam Showalter, Mark Steyvers, Padhraic Smyth Published: 2025-06-05T23:39:41.000Z ## Abstract Applications of machine learning often involve making predictions based on both model outputs and the opinions of human experts. In this context, we investigate the problem of querying experts for class label predictions, using as few human queries as possible, and leveraging the class probability estimates of pre-trained classifiers. We develop a general Bayesian framework for this problem, modeling expert correlation via a joint latent representation, enabling simulation-based inference about the utility of additional expert queries, as well as inference of posterior distributions over unobserved expert labels. We apply our approach to two real-world medical classification problems, as well as to CIFAR-10H and ImageNet-16H, demonstrating substantial reductions relative to baselines in the cost of querying human experts while maintaining high prediction accuracy. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()