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Found 12 papers, 7 papers with code
A deep generative model for gene expression profiles from single-cell RNA sequencing
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.
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing.
Information Constraints on Auto-Encoding Variational Bayes
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 Semi-Supervised Classification with Noisy Labels
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes.
Cost-Effective Incentive Allocation via Structured Counterfactual Inference
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.
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
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.
Decision-Making with Auto-Encoding Variational Bayes
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.
Learning from eXtreme Bandit Feedback
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.
Large-Scale Differentiable Causal Discovery of Factor Graphs
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.
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
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.
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science.
Toward the Identifiability of Comparative Deep Generative Models
Finally, we introduce a novel methodology for fitting comparative DGMs that improves the treatment of multiple data sources via multi-objective optimization and that helps adjust the hyperparameter for the regularization in an interpretable manner, using constrained optimization.
