Warren Morningstar — accepted AI-conference papers

2021

Automatic Differentiation Variational Inference with Mixtures

Warren Morningstar, Sharad Vikram, Cusuh Ham, Andrew Gallagher, Joshua Dillon

AISTATS 2021poster

Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning. Generally approximate posteriors learned by ADVI are forced to be unimodal in order to facilitate use of the reparameterization trick. In this paper, we show how…

Cited by 31 Source ↗PDF ↗Scholar ↗

2021

Density of States Estimation for Out of Distribution Detection

Warren Morningstar, Cusuh Ham, Andrew Gallagher, Balaji Lakshminarayanan, Alex Alemi, Joshua Dillon

AISTATS 2021poster

Perhaps surprisingly, recent studies have shown probabilistic model likelihoods have poor specificity for out-of-distribution (OOD) detection and often assign higher likelihoods to OOD data than in-distribution data. To ameliorate this issue we propose DoSE, the density of states estimator. Drawing…

Cited by 108 Source ↗PDF ↗Scholar ↗