Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data
Causal discovery methods typically extract causal relations between multiple nodes
(variables) based on univariate observations of each node. However, one frequently …
(variables) based on univariate observations of each node. However, one frequently …
Joint inference of multiple graphs from matrix polynomials
Inferring graph structure from observations on the nodes is an important and popular
network science task. Departing from the more common inference of a single graph, we …
network science task. Departing from the more common inference of a single graph, we …
Estimation of partially known Gaussian graphical models with score-based structural priors
We propose a novel algorithm for the support estimation of partially known Gaussian
graphical models that incorporates prior information about the underlying graph. In contrast …
graphical models that incorporates prior information about the underlying graph. In contrast …
Joint network topology inference via a shared graphon model
We consider the problem of estimating the topology of multiple networks from nodal
observations, where these networks are assumed to be drawn from the same (unknown) …
observations, where these networks are assumed to be drawn from the same (unknown) …
Multi-task learning of order-consistent causal graphs
We consider the problem of discovering $ K $ related Gaussian directed acyclic graphs
(DAGs), where the involved graph structures share a consistent causal order and sparse …
(DAGs), where the involved graph structures share a consistent causal order and sparse …
Joint network topology inference in the presence of hidden nodes
We investigate the increasingly prominent task of jointly inferring multiple networks from
nodal observations. While most joint inference methods assume that observations are …
nodal observations. While most joint inference methods assume that observations are …
Causal structure discovery from distributions arising from mixtures of dags
We consider distributions arising from a mixture of causal models, where each model is
represented by a directed acyclic graph (DAG). We provide a graphical representation of …
represented by a directed acyclic graph (DAG). We provide a graphical representation of …
Machine learning approaches to single-cell data integration and translation
Experimental single-cell data often presents an incomplete picture due to its destructive
nature: 1) we collect certain experimental measurements of cells but lack measurements …
nature: 1) we collect certain experimental measurements of cells but lack measurements …
Joint inference of multiple graphs with hidden variables from stationary graph signals
Learning graphs from sets of nodal observations represents a prominent problem formally
known as graph topology inference. However, current approaches are limited by typically …
known as graph topology inference. However, current approaches are limited by typically …
Joint bayesian-incorporating estimation of multiple gaussian graphical models to study brain connectivity development in adolescence
Adolescence is a transitional period between the childhood and adulthood with physical
changes, as well as increasing emotional development. Studies have shown that the …
changes, as well as increasing emotional development. Studies have shown that the …