# A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance

arXiv: https://arxiv.org/abs/2211.14061 · HTML: https://mlsanity.com/paper/2211.14061/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 92 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 24 of 24)

- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [Deep Learning Scaling is Predictable, Empirically](https://mlsanity.com/paper/1712.00409/) (2017)
- [Surprises in High-Dimensional Ridgeless Least Squares Interpolation](https://mlsanity.com/paper/1903.08560/) (2019)
- [High-dimensional dynamics of generalization error in neural networks](https://mlsanity.com/paper/1710.03667/) (2017)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [Optimal Regularization Can Mitigate Double Descent](https://mlsanity.com/paper/2003.01897/) (2020)
- [Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It](https://mlsanity.com/paper/1412.3730/) (2014)
- [The Shape of Learning Curves: a Review](https://mlsanity.com/paper/2103.10948/) (2021)
- [A jamming transition from under- to over-parametrization affects loss landscape and generalization](https://mlsanity.com/paper/1810.09665/) (2018)
- [Triple descent and the two kinds of overfitting: Where & why do they appear?](https://mlsanity.com/paper/2006.03509/) (2020)
- [A Brief Prehistory of Double Descent](https://mlsanity.com/paper/2004.04328/) (2020)
- [More Data Can Hurt for Linear Regression: Sample-wise Double Descent](https://mlsanity.com/paper/1912.07242/) (2019)
- [Learning Curves for Decision Making in Supervised Machine Learning: A Survey](https://mlsanity.com/paper/2201.12150/) (2022)
- [Learning Curves for Analysis of Deep Networks](https://mlsanity.com/paper/2010.11029/) (2020)
- [A critical analysis of metrics used for measuring progress in artificial intelligence](https://mlsanity.com/paper/2008.02577/) (2020)
- [Introducing Geometry in Active Learning for Image Segmentation](https://mlsanity.com/paper/1508.04955/) (2015)
- [Minimizers of the Empirical Risk and Risk Monotonicity](https://mlsanity.com/paper/1907.05476/) (2019)
- [Risk-Monotonicity in Statistical Learning](https://mlsanity.com/paper/2011.14126/) (2020)
- [Monotone Learning](https://mlsanity.com/paper/2202.05246/) (2022)
- [Making Learners (More) Monotone](https://mlsanity.com/paper/1911.11030/) (2019)
- [The Peaking Phenomenon in Semi-supervised Learning](https://mlsanity.com/paper/1610.05160/) (2016)
- [A universally consistent learning rule with a universally monotone error](https://mlsanity.com/paper/2108.09733/) (2021)
- [Supervised Classification: Quite a Brief Overview](https://mlsanity.com/paper/1710.09230/) (2017)

## Built on by (most cited first, 2 of 2)

- [TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins](https://mlsanity.com/paper/2606.17660/) (2026)
- [Data-Efficient Deep Learning: Empirical Guidelines for Training Set Size Estimation in Inertial Sensor Classification](https://mlsanity.com/paper/2607.09402/) (2026)

## Abstract (arXiv)

Plotting a learner's generalization performance against the training set size results in a so-called learning curve. This tool, providing insight in the behavior of the learner, is also practically valuable for model selection, predicting the effect of more training data, and reducing the computational complexity of training. We set out to make the (ideal) learning curve concept precise and briefly discuss the aforementioned usages of such curves. The larger part of this survey's focus, however, is on learning curves that show that more data does not necessarily leads to better generalization performance. A result that seems surprising to many researchers in the field of artificial intelligence. We point out the significance of these findings and conclude our survey with an overview and discussion of open problems in this area that warrant further theoretical and empirical investigation.