Hyperparameter Optimization with Differentiable Metafeatures

• URL: http://arxiv.org/abs/2102.03776v1
• Date: Sun, 7 Feb 2021 11:06:31 GMT
• Title: Hyperparameter Optimization with Differentiable Metafeatures
• Authors: Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka
• Abstract summary: We propose a cross dataset surrogate model called Differentiable Metafeature-based Surrogate (DMFBS) In contrast to existing models, DMFBS i) integrates a differentiable metafeature extractor and ii) is optimized using a novel multi-task loss. We compare DMFBS against several recent models for HPO on three large meta-datasets and show that it consistently outperforms all of them with an average 10% improvement.
• Score: 5.586191108738563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metafeatures, or dataset characteristics, have been shown to improve the performance of hyperparameter optimization (HPO). Conventionally, metafeatures are precomputed and used to measure the similarity between datasets, leading to a better initialization of HPO models. In this paper, we propose a cross dataset surrogate model called Differentiable Metafeature-based Surrogate (DMFBS), that predicts the hyperparameter response, i.e. validation loss, of a model trained on the dataset at hand. In contrast to existing models, DMFBS i) integrates a differentiable metafeature extractor and ii) is optimized using a novel multi-task loss, linking manifold regularization with a dataset similarity measure learned via an auxiliary dataset identification meta-task, effectively enforcing the response approximation for similar datasets to be similar. We compare DMFBS against several recent models for HPO on three large meta-datasets and show that it consistently outperforms all of them with an average 10% improvement. Finally, we provide an extensive ablation study that examines the different components of our approach.

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