368 citations · 532 across the 14 of their papers we have counts for
15 papers
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation
Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet +2
We introduce ordered transfer hyperparameter optimisation (OTHPO), a version of transfer learning for hyperparameter optimisation (HPO) where the tasks follow a sequential order. U…
Optimizing Hyperparameters with Conformal Quantile Regression
David Salinas, Jacek Golebiowski, Aaron Klein +2
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian p…
Criteria for Classifying Forecasting Methods
Tim Januschowski, Jan Gasthaus, Yuyang Wang +4
Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exem…
Multi-Objective Model Selection for Time Series Forecasting
Oliver Borchert, David Salinas, Valentin Flunkert +2
Research on time series forecasting has predominantly focused on developing methods that improve accuracy. However, other criteria such as training time or latency are critical in…
Meta-Forecasting by combining Global Deep Representations with Local Adaptation
Riccardo Grazzi, Valentin Flunkert, David Salinas +3
While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of rela…
Multi-objective Asynchronous Successive Halving
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar +2
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e.g., accuracy) of machine learning models. However, in a plethora of real-…