arXiv:2111.00513
공식 코드 · Papers with Code
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup
원문 초록
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.
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BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL
Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints
HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings
Clustering-based Meta Bayesian Optimization with Theoretical Guarantee
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning
MOHPER: Multi-objective Hyperparameter Optimization Framework for E-commerce Retrieval System
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