Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup
This work addresses hyperparameter optimization for industrial tasks in a competition setting, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the automated hyperparameter optimization challenge in the QQ Browser 2021 AI Algorithm Competition by using their open-sourced package OpenBox with Bayesian optimization and a heuristic early stopping strategy, achieving first place with scores of 0.938291 and 0.918753 in the preliminary and final contests.
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.