Search Results for author:
Found 2 papers, 1 papers with code
Learning from Aggregate Observations
We study the problem of learning from aggregate observations where supervision signals are given to sets of instances instead of individual instances, while the goal is still to predict labels of unseen individuals.
Learning with Proper Partial Labels
Recently, various approaches on partial-label learning have been proposed under different generation models of candidate label sets.
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