mlsanityData to 7 Oct 2026

ChaCha for Online AutoML

arXiv 2106.04815 ↗First posted 9 Jun 2021cs.LGcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 9 Jun 2021.

Builds on

14 of 34 references.

References · 14

  1. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
  2. 2014OpenML: networked science in machine learningcited by 523
  3. 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 466
  4. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  5. 2020AutoML-Zero: Evolving Machine Learning Algorithms From Scratchcited by 137
  6. 2016Coin Betting and Parameter-Free Online Learningcited by 131
  7. 2019FLAML: A Fast and Lightweight AutoML Librarycited by 104
  8. 2019AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applicationscited by 39
  9. 2017Parameter-free online learning via model selectioncited by 34
  10. 2018Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuningcited by 20
  11. 2015Selecting Near-Optimal Learners via Incremental Data Allocationcited by 12
  12. 2018Best of many worlds: Robust model selection for online supervised learningcited by 5
2 more
  1. 2021Model Selection for Production System via Automated Online Experimentscited by 2
  2. 2014Scalable Nonlinear Learning with Adaptive Polynomial Expansionscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2022Online AutoML: An adaptive AutoML framework for online learningcited by 11
  2. 2022Mining Robust Default Configurations for Resource-constrained AutoMLcited by 2
  3. 2026OpenLoopEvolve: A Verifiable Self-Evolution Framework for Loop Policies in Long-Horizon Complex Taskscited by 0