# Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round

arXiv: https://arxiv.org/abs/2208.02821 · HTML: https://mlsanity.com/paper/2208.02821/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Freeze-Thaw Bayesian Optimization](https://mlsanity.com/paper/1406.3896/) (2014)
- [Few-Shot Bayesian Optimization with Deep Kernel Surrogates](https://mlsanity.com/paper/2101.07667/) (2021)
- [Learning Curves for Decision Making in Supervised Machine Learning: A Survey](https://mlsanity.com/paper/2201.12150/) (2022)
- [Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019](https://mlsanity.com/paper/2201.03801/) (2022)
- [Advances in MetaDL: AAAI 2021 challenge and workshop](https://mlsanity.com/paper/2202.01890/) (2022)

## Built on by (most cited first, 1 of 1)

- [Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection](https://mlsanity.com/paper/2410.07696/) (2024)

## Abstract (arXiv)

Meta-learning from learning curves is an important yet often neglected research area in the Machine Learning community. We introduce a series of Reinforcement Learning-based meta-learning challenges, in which an agent searches for the best suited algorithm for a given dataset, based on feedback of learning curves from the environment. The first round attracted participants both from academia and industry. This paper analyzes the results of the first round (accepted to the competition program of WCCI 2022), to draw insights into what makes a meta-learner successful at learning from learning curves. With the lessons learned from the first round and the feedback from the participants, we have designed the second round of our challenge with a new protocol and a new meta-dataset. The second round of our challenge is accepted at the AutoML-Conf 2022 and currently ongoing .