# ChaCha for Online AutoML

arXiv: https://arxiv.org/abs/2106.04815 · HTML: https://mlsanity.com/paper/2106.04815/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch](https://mlsanity.com/paper/2003.03384/) (2020)
- [Coin Betting and Parameter-Free Online Learning](https://mlsanity.com/paper/1602.04128/) (2016)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications](https://mlsanity.com/paper/1904.12857/) (2019)
- [Parameter-free online learning via model selection](https://mlsanity.com/paper/1801.00101/) (2017)
- [Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning](https://mlsanity.com/paper/1804.07824/) (2018)
- [Selecting Near-Optimal Learners via Incremental Data Allocation](https://mlsanity.com/paper/1601.00024/) (2015)
- [Best of many worlds: Robust model selection for online supervised learning](https://mlsanity.com/paper/1805.08562/) (2018)
- [Model Selection for Production System via Automated Online Experiments](https://mlsanity.com/paper/2105.13420/) (2021)
- [Scalable Nonlinear Learning with Adaptive Polynomial Expansions](https://mlsanity.com/paper/1410.0440/) (2014)

## Built on by (most cited first, 3 of 3)

- [Online AutoML: An adaptive AutoML framework for online learning](https://mlsanity.com/paper/2201.09750/) (2022)
- [Mining Robust Default Configurations for Resource-constrained AutoML](https://mlsanity.com/paper/2202.09927/) (2022)
- [OpenLoopEvolve: A Verifiable Self-Evolution Framework for Loop Policies in Long-Horizon Complex Tasks](https://mlsanity.com/paper/2608.09380/) (2026)

## Abstract (arXiv)

We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champion and scheduling a set of `live' challengers over time based on sample complexity bounds. It is guaranteed to have sublinear regret after the optimal configuration is added into consideration by an application-dependent oracle based on the champions. Empirically, we show that ChaCha provides good performance across a wide array of datasets when optimizing over featurization and hyperparameter decisions.