# Making Learners (More) Monotone

arXiv: https://arxiv.org/abs/1911.11030 · HTML: https://mlsanity.com/paper/1911.11030/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [The Shape of Learning Curves: a Review](https://mlsanity.com/paper/2103.10948/) (2021)
- [Learning Curves for Decision Making in Supervised Machine Learning: A Survey](https://mlsanity.com/paper/2201.12150/) (2022)
- [Monotone Learning](https://mlsanity.com/paper/2202.05246/) (2022)
- [A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance](https://mlsanity.com/paper/2211.14061/) (2022)
- [Monotonic Learning in the PAC Framework: A New Perspective](https://mlsanity.com/paper/2501.05493/) (2025)
- [Risk reversal for least squares estimators under nested convex constraints](https://mlsanity.com/paper/2601.16041/) (2026)

## Abstract (arXiv)

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.