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cs.LG2021★ 1 cited
HyperJump: Accelerating HyperBand via Risk Modelling
Pedro Mendes, Maria Casimiro, Paolo Romano +1
In the literature on hyper-parameter tuning, a number of recent solutions rely on low-fidelity observations (e.g., training with sub-sampled datasets) in order to efficiently ident…
cs.LG2020
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling
Pedro Mendes, Maria Casimiro, Paolo Romano +1
This work introduces TrimTuner, the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques to reduce the cost of the optimization process…