Making Learners (More) Monotone

Making Learners (More) Monotone

Viering, T.J. (TU Delft Pattern Recognition and Bioinformatics)
Mey, A. (TU Delft Interactive Intelligence)
Loog, M. (TU Delft Pattern Recognition and Bioinformatics; University of Copenhagen)

Berthold, Michael R. (editor)
Feelders, Ad (editor)
Krempl, Georg (editor)

2020

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. Our code is available at https://github.com/tomviering/monotone.

Learning curve
Learning theory
Model selection

http://resolver.tudelft.nl/uuid:d9cadba6-fc8c-4374-9517-412eff8d1bde

https://doi.org/10.1007/978-3-030-44584-3_42

SpringerOpen, Cham

978-3-030-44583-6

Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings, 12080

18th International Conference on Intelligent Data Analysis, IDA 2020, 2020-04-27 → 2020-04-29, Konstanz, Germany

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12080

Virtual/online event due to COVID-19

Institutional Repository

conference paper

© 2020 T.J. Viering, A. Mey, M. Loog