ACHO: Adaptive Conformal Hyperparameter Optimization
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Abstract (arXiv)
Several novel frameworks for hyperparameter search have emerged in the last decade, but most rely on strict, often normal, distributional assumptions, limiting search model flexibility. This paper proposes a novel optimization framework based on upper confidence bound sampling of conformal confidence intervals, whose weaker assumption of exchangeability enables greater choice of search model architectures. Several such architectures were explored and benchmarked on hyperparameter search of random forests and convolutional neural networks, displaying satisfactory interval coverage and superior tuning performance to random search.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 6 Jul 2022.
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References · 10
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 688
- 2016Distribution-Free Predictive Inference For Regressioncited by 657
- 2019Conformalized Quantile Regressioncited by 620
- 2021Adaptive Conformal Inference Under Distribution Shiftcited by 360
- 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 146
- 2013A Conformal Prediction Approach to Explore Functional Datacited by 85
- 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 66
- 2014Sequential Model-Based Ensemble Optimizationcited by 3
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