First strongly Bayes-consistent algorithm for metric-valued regression with unbounded loss in the agnostic setting, based on metric medoids and semi-stable compression.
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A Fréchet-based random-effects algorithm with M-estimation consistency guarantees is proposed for modeling non-Euclidean random objects in general metric spaces.
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Metric-valued regression
First strongly Bayes-consistent algorithm for metric-valued regression with unbounded loss in the agnostic setting, based on metric medoids and semi-stable compression.