论文标题
从学习曲线挑战的元学习挑战：从第一轮中学到的经验教训和第二轮的设计
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round
论文作者
论文摘要
学习曲线的元学习是机器学习社区中一个重要但经常被忽视的研究领域。我们介绍了一系列基于增强学习的元学习挑战，在这些挑战中，代理商根据来自环境的学习曲线的反馈来寻找适合给定数据集的最佳算法。第一轮吸引了学术界和行业的参与者。本文分析了第一轮的结果（被WCCI 2022的竞争计划所接受），以了解使元学习曲线成功地从学习曲线学习的东西。通过从第一轮中学到的教训以及参与者的反馈，我们通过新的协议和新的元数据设计设计了第二轮挑战。我们的挑战的第二轮在2022年的Automl-Conf中被接受，目前正在进行中。
Meta-learning from learning curves is an important yet often neglected research area in the Machine Learning community. We introduce a series of Reinforcement Learning-based meta-learning challenges, in which an agent searches for the best suited algorithm for a given dataset, based on feedback of learning curves from the environment. The first round attracted participants both from academia and industry. This paper analyzes the results of the first round (accepted to the competition program of WCCI 2022), to draw insights into what makes a meta-learner successful at learning from learning curves. With the lessons learned from the first round and the feedback from the participants, we have designed the second round of our challenge with a new protocol and a new meta-dataset. The second round of our challenge is accepted at the AutoML-Conf 2022 and currently ongoing .
