35 citations · 55 across the 7 of their papers we have counts for
4 papers · 1 filter
Improving Hyperparameter Optimization by Planning Ahead
Hadi S. Jomaa, Jonas Falkner, Lars Schmidt-Thieme
Hyperparameter optimization (HPO) is generally treated as a bi-level optimization problem that involves fitting a (probabilistic) surrogate model to a set of observed hyperparamete…
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Sebastian Pineda Arango, Hadi S. Jomaa, Martin Wistuba +1
Hyperparameter optimization (HPO) is a core problem for the machine learning community and remains largely unsolved due to the significant computational resources required to evalu…
Hyperparameter Optimization with Differentiable Metafeatures
Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka
Metafeatures, or dataset characteristics, have been shown to improve the performance of hyperparameter optimization (HPO). Conventionally, metafeatures are precomputed and used to…
Do We Really Need Deep Learning Models for Time Series Forecasting?
Shereen Elsayed, Daniela Thyssens, Ahmed Rashed +2
Time series forecasting is a crucial task in machine learning, as it has a wide range of applications including but not limited to forecasting electricity consumption, traffic, and…