Ann B. Lee — accepted AI-conference papers

2024

Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference

Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee

ICML 2024poster

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of dist…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2021

Diagnostics for conditional density models and Bayesian inference algorithms

David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee

UAI 2021poster

There has been growing interest in the AI community for precise uncertainty quantification. Conditional density models f(y|x), where x represents potentially high-dimensional features, are an integral part of uncertainty quantification in prediction and Bayesian inference. However, it is challenging…

Cited by 36 Source ↗PDF ↗code ↗Scholar ↗