# Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup

arXiv: https://arxiv.org/abs/2111.00513 · HTML: https://mlsanity.com/paper/2111.00513/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [OpenBox: A Generalized Black-box Optimization Service](https://mlsanity.com/paper/2106.00421/) (2021)

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

- [OpenBox: A Python Toolkit for Generalized Black-box Optimization](https://mlsanity.com/paper/2304.13339/) (2023)
- [Rover: An online Spark SQL tuning service via generalized transfer learning](https://mlsanity.com/paper/2302.04046/) (2023)
- [Efficient End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2206.09423/) (2022)

## Abstract (arXiv)

In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2). The competition organizers provide anonymized realistic industrial tasks and datasets for black-box optimization. Based on our open-sourced package OpenBox, we adopt the Bayesian optimization framework for configuration sampling and a heuristic early stopping strategy. We won first place in both the preliminary and final contests with the results of 0.938291 and 0.918753, respectively.