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September 10, 2025Open Access

Differentiable Subset Selection for Cardinality-Constrained Bayesian Optimization

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Authors

DFDaisuke FukushimaTokyo Institute of Technology YKYuki KuritaHamamatsu University

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Implication

This paper demonstrates a novel differentiable approach to optimize acquisition functions under cardinality constraints, enhancing exploration efficiency.

Key Points

The proposed framework enables efficient optimization of Bayesian optimization under cardinality constraints, improving variable selection.
Utilizing Gumbel-Top-k sampling with the Straight-Through Estimator leads to better handling of k-sparsity during optimization.
Experiments on synthetic data reveal superior performance in optimization quality compared to conventional methods.
An alternative kernel formulation addresses limitations of the Matérn kernel, allowing for effective exploration in sparse selection tasks.

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Fukushima et al. (2025) studied this question.

synapsesocial.com/papers/68c1b18554b1d3bfb60e841c https://doi.org/10.36227/techrxiv.175432916.60787000/v1

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Implication

This paper demonstrates a novel differentiable approach to optimize acquisition functions under cardinality constraints, enhancing exploration efficiency.

Differentiable Subset Selection for Cardinality-Constrained Bayesian Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

Differentiable Subset Selection for Cardinality-Constrained Bayesian Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

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