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stat.ML2019
Ancestral causal learning in high dimensions with a human genome-wide application
Umberto Noè, Bernd Taschler, Joachim Täger +2
We consider learning ancestral causal relationships in high dimensions. Our approach is driven by a supervised learning perspective, with discrete indicators of causal relationship…
stat.ML2018
On a New Improvement-Based Acquisition Function for Bayesian Optimization
Umberto Noè, Dirk Husmeier
Bayesian optimization (BO) is a popular algorithm for solving challenging optimization tasks. It is designed for problems where the objective function is expensive to evaluate, per…