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stat.ML2024
SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals
Ilia Azizi, Marc-Olivier Boldi, Valérie Chavez-Demoulin
This work introduces the Supervised Expectation-Maximization Framework (SEMF), a versatile and model-agnostic approach for generating prediction intervals with any ML model. SEMF e…
stat.ML2018★ 5 cited
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter
Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence…