Search Results for author: Romain Lopez

Found 8 papers, 4 papers with code

Learning from eXtreme Bandit Feedback

no code implementations27 Sep 2020 Romain Lopez, Inderjit S. Dhillon, Michael. I. Jordan

In POXM, the selected actions for the sIS estimator are the top-p actions of the logging policy, where p is adjusted from the data and is significantly smaller than the size of the action space.

Extreme Multi-Label Classification Multi-Label Classification +1

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

3 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

Cost-Effective Incentive Allocation via Structured Counterfactual Inference

no code implementations7 Feb 2019 Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael. I. Jordan, Yuan Qi, Le Song

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback.

Counterfactual Inference Domain Adaptation

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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