Making Learners (More) Monotone
Tom Viering (TU Delft - Pattern Recognition and Bioinformatics)
A. Mey (TU Delft - Interactive Intelligence)
Marco Loog (University of Copenhagen, TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Learning performance can show non-monotonic behavior. That is, more data does not necessarily lead to better models, even on average. We propose three algorithms that take a supervised learning model and make it perform more monotone. We prove consistency and monotonicity with high probability, and evaluate the algorithms on scenarios where non-monotone behaviour occurs. Our proposed algorithm MTHT makes less than 1% non-monotone decisions on MNIST while staying competitive in terms of error rate compared to several baselines. Our code is available at https://github.com/tomviering/monotone.
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