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subeeshvasu / Awesome-Learning-with-Label-Noise

A curated list of resources for Learning with Noisy Labels
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learning success rates with variable number of attempts #28

Open cadama opened 3 years ago

cadama commented 3 years ago

I am working on a problem where the labels/response variables take the form of #successes / #attempts. Clearly the goodness of the label depends on the number of attempt so I'd like to avoid the model to learn corner cases like y=0, y=1 that essentially occur because not enough attempts have been made.

We generally frame this problem as either a regression task with mse loss and weights given by #attempts or by looking at it as a classification task with label in [0, 1] and weights equal to #attempts - #successes and #successes respectively and trained through binary cross entropy.

Do you have any paper to recommend that tackle this problem? thanks in advance

subeeshvasu commented 3 years ago

HI, I am not familiar with any papers on this topic, but some others might know. So let's keep this issue open.