c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization
arXiv:2211.14411v5 Announce Type: replace Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as on memory usage or latency, on top of the performance requirement. In this work, we propose constrained TPE (c-TPE), an extension of the widely-used versatile Bayesian optimization method, tree-structured Parzen estimator (TPE), to handle these constraints. Our proposed extension goes beyond a simple combination of an existing acquisition function and the original TPE, and instead i
The continuous drive for more efficient and performant deep learning models necessitates advanced hyperparameter optimization techniques, especially as real-world applications impose tighter constraints.
Improving hyperparameter optimization with constraint handling will enhance the practical applicability and efficiency of AI, benefiting sectors reliant on complex machine learning deployments.
The proposed c-TPE method provides a more robust and efficient way to optimize deep learning models under realistic operational constraints like memory or latency, moving beyond simple performance maximization.
- · Deep learning developers
- · AI-driven industries
- · Hardware manufacturers
- · Cloud computing providers
- · Less efficient HPO methods
- · Manual hyperparameter tuning
More resource-efficient and performant AI models become deployable in a wider array of constrained environments.
Reduced operational costs and faster iteration cycles for AI development, accelerating innovation in various applications.
Enhanced accessibility and democraticization of advanced AI for organizations with limited compute resources, potentially fostering new AI applications.
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Read at arXiv cs.LG