Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?

• URL: http://arxiv.org/abs/2507.12604v1
• Date: Wed, 16 Jul 2025 19:50:28 GMT
• Title: Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?
• Authors: Antoni Zajko, Katarzyna Woźnica,
• Abstract summary: We propose two novel methods for representation learning tailored to a specific meta-task.<n>First approach involves deep metric learning, while the second one is based on landmarkers reconstruction.<n>Experiments demonstrate that while the proposed encoders can effectively learn representations aligned with landmarkers, they may not directly translate to significant performance gains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effectively representing heterogeneous tabular datasets for meta-learning purposes is still an open problem. Previous approaches rely on representations that are intended to be universal. This paper proposes two novel methods for tabular representation learning tailored to a specific meta-task - warm-starting Bayesian Hyperparameter Optimization. Both follow the specific requirement formulated by ourselves that enforces representations to capture the properties of landmarkers. The first approach involves deep metric learning, while the second one is based on landmarkers reconstruction. We evaluate the proposed encoders in two ways. Next to the gain in the target meta-task, we also use the degree of fulfillment of the proposed requirement as the evaluation metric. Experiments demonstrate that while the proposed encoders can effectively learn representations aligned with landmarkers, they may not directly translate to significant performance gains in the meta-task of HPO warm-starting.

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