Making Learners (More) Monotone.

Tom Julian Viering, Alexander Mey, Marco Loog

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 $$\text {MT}_{\text {HT}}$$ 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 .