Search Results for author: Nir Yosef

Found 7 papers, 5 papers with code

AutoEval Done Right: Using Synthetic Data for Model Evaluation

1 code implementation9 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.

Decision-Making with Auto-Encoding Variational Bayes

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.

Decision Making Two-sample testing

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

2 code implementations6 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.

Domain Adaptation Imputation

Information Constraints on Auto-Encoding Variational Bayes

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.

A deep generative model for gene expression profiles from single-cell RNA sequencing

2 code implementations7 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.

Stochastic Optimization Variational Inference

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