https://concept-drift.fastforwardlabs.com/ Inferring Concept Drift Without Labeled DataFF22Aug 2021 “…we explore broadly applicable approaches for dealing with concept drift when labeled data is not readily accessible. We’ll start by defining what
What I Read: forecasting, quantile functions
https://www.amazon.science/blog/improving-forecasting-by-learning-quantile-functions Improving forecasting by learning quantile functionsBy Youngsuk Park, François-Xavier AubetMarch 31, 2022 “Learning the complete quantile function, which maps probabilities to variable values, rather than building separate models for
What I Read: binary cross-entropy, log loss
https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a?gi=375ce73be21b Understanding binary cross-entropy / log loss: a visual explanationDaniel GodoyNov 21, 2018 “If you are training a binary classifier, chances are you are using binary cross-entropy / log loss
What I Read: learning-to-rank
https://www.amazon.science/blog/using-learning-to-rank-to-precisely-locate-where-to-deliver-packages Using learning-to-rank to precisely locate where to deliver packagesModels adapted from information retrieval deal well with noisy GPS input and can leverage map information.By George FormanSeptember 15, 2021 “For