Sebastian Salazar — accepted AI-conference papers

2025

Posterior Mean Matching: Generative Modeling through Online Bayesian Inference

Sebastian Salazar, Michal Kucer, Yixin Wang, Emily Casleton, David Blei

AISTATS 2025poster

This paper introduces posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model complex data of various modalities like images and text, offering a flexible alternative to existing methods like diffu…

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2023

VaRT: Variational Regression Trees

Sebastian Salazar

NeurIPS 2023spotlight

Decision trees are a well-established tool in machine learning for classification and regression tasks. In this paper, we introduce a novel non-parametric Bayesian model that uses variational inference to approximate a posterior distribution over the space of stochastic decision trees. We evaluate t…

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