Making Learners (More) Monotone
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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 MT_HT makes less than 1% non-monotone decisions on MNIST while staying competitive in terms of error rate compared to several baselines.
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