no code implementations • 4 Jan 2023 • Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter
Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science.
1 code implementation • 7 Nov 2022 • Romain Lopez, Nataša Tagasovska, Stephen Ra, Kyunghyn Cho, Jonathan K. Pritchard, Aviv Regev
Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic.
1 code implementation • 15 Jun 2022 • Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev
Combining this novel structural assumption with recent advances that bridge the gap between causal discovery and continuous optimization, we achieve causal discovery on thousands of variables.
no code implementations • 27 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.
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.
no code implementations • 7 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.
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.