3 accepted papers
We introduce DeCaFlow, a deconfounding causal generative model. Training once per dataset using just observational data and the underlying causal graph, DeCaFlow enables accurate causal inference on continuous variables under the presence of hidden confounders. Specifically, we extend previous resul…
We study an underwater navigation problem, where an Under-water Autonomous Vehicle must reach a target position in the presence of a disturbance that may be unknown. In order to deal with this problem, we make use of Deep Reinforcement Learning tools, and more concretely, we make use of robust contr…
In this work, we develop an algorithm based on graph networks to train distributedly a deep learning model. We consider that there are several nodes, in an arbitrary network topology, each one of them having access to a local dataset that, for privacy concerns, cannot be shared with other nodes. We…