1 citations · 2 across the 5 of their papers we have counts for
5 papers
Unguided structure learning of DAGs for count data
Thi Kim Hue Nguyen, Monica Chiogna, Davide Risso
Mainly motivated by the problem of modelling directional dependence relationships for multivariate count data in high-dimensional settings, we present a new algorithm, called learn…
Heteroscedastic Causal Structure Learning
Bao Duong, Thin Nguyen
Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent…
Conditional Independence Testing via Latent Representation Learning
Bao Duong, Thin Nguyen
Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms. In this study, we introduce LCIT…
Guided structure learning of DAGs for count data
Thi Kim Hue Nguyen, Monica Chiogna, Davide Risso +1
In this paper, we tackle structure learning of Directed Acyclic Graphs (DAGs), with the idea of exploiting available prior knowledge of the domain at hand to guide the search of th…
Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data
Thi Kim Hue Nguyen, Koen Van den Berge, Monica Chiogna +1
The problem of estimating the structure of a graph from observed data is of growing interest in the context of high-throughput genomic data, and single-cell RNA sequencing in parti…