93 citations · 305 across the 23 of their papers we have counts for
9 papers · 1 filter
Dynamic Survival Transformers for Causal Inference with Electronic Health Records
Prayag Chatha, Yixin Wang, Zhenke Wu +1
In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes. However, the standard methods for causal survival analysis make simplistic assu…
An Empirical Comparison of GANs and Normalizing Flows for Density Estimation
Tianci Liu, Jeffrey Regier
Generative adversarial networks (GANs) and normalizing flows are both approaches to density estimation that use deep neural networks to transform samples from an uninformative prio…
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
Romain Lopez, Achille Nazaret, Maxime Langevin +4
Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous and complex tissues. However, most experimental protocols for spatial transcriptomics…
A Deep Generative Model for Semi-Supervised Classification with Noisy Labels
Maxime Langevin, Edouard Mehlman, Jeffrey Regier +3
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes. We propose a new semi-supervised deep generative model that explicitly models n…
Information Constraints on Auto-Encoding Variational Bayes
Romain Lopez, Jeffrey Regier, Michael I. Jordan +1
Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibi…
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Nilesh Tripuraneni, Mitchell Stern, Chi Jin +2
This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak 2006]. The proposed algorithm efficiently escapes saddle…
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes
Romain Lopez, Jeffrey Regier, Michael Cole +2
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent…
A deep generative model for gene expression profiles from single-cell RNA sequencing
Romain Lopez, Jeffrey Regier, Michael Cole +2
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent…
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Jeffrey Regier, Michael I. Jordan, Jon McAuliffe
We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick. At each iteration, Trus…