1 code implementation • 9 Mar 2024 • Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, Michael I. Jordan
The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming.
2 code implementations • NeurIPS 2020 • Romain Lopez, Pierre Boyeau, Nir Yosef, Michael. I. Jordan, Jeffrey Regier
To make decisions based on a model fit with auto-encoding variational Bayes (AEVB), practitioners often let the variational distribution serve as a surrogate for the posterior distribution.
2 code implementations • 6 May 2019 • Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael. I. Jordan, Nir Yosef
Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes.
1 code implementation • 16 Sep 2018 • Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael. I. Jordan, Nir Yosef
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes.
no code implementations • NeurIPS 2018 • Romain Lopez, Jeffrey Regier, Michael. I. Jordan, Nir Yosef
We show how to apply this method to a range of problems, including the problems of learning invariant representations and the learning of interpretable representations.
no code implementations • 13 Oct 2017 • Romain Lopez, Jeffrey Regier, Michael Cole, Michael Jordan, Nir Yosef
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing.
2 code implementations • 7 Sep 2017 • Romain Lopez, Jeffrey Regier, Michael Cole, Michael Jordan, Nir Yosef
We also extend our framework to account for batch effects and other confounding factors, and propose a Bayesian hypothesis test for differential expression that outperforms DESeq2.