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Found 8 papers, 7 papers with code
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
In this work, we propose FT-PFN, a novel surrogate for Freeze-thaw style BO.
Fast Benchmarking of Asynchronous Multi-Fidelity Optimization on Zero-Cost Benchmarks
While deep learning has celebrated many successes, its results often hinge on the meticulous selection of hyperparameters (HPs).
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance.
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications.
DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization
Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important than ever.
Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge.
Differential Evolution for Neural Architecture Search
Neural architecture search (NAS) methods rely on a search strategy for deciding which architectures to evaluate next and a performance estimation strategy for assessing their performance (e. g., using full evaluations, multi-fidelity evaluations, or the one-shot model).
OpenML-Python: an extensible Python API for OpenML
It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML.
