208 citations · 394 across the 12 of their papers we have counts for
15 papers
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…
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi +3
Neural Architecture Search (NAS) methods have been growing in popularity. These techniques have been fundamental to automate and speed up the time consuming and error-prone process…
Few-Shot Bayesian Optimization with Deep Kernel Surrogates
Martin Wistuba, Josif Grabocka
Hyperparameter optimization (HPO) is a central pillar in the automation of machine learning solutions and is mainly performed via Bayesian optimization, where a parametric surrogat…
Learning to Rank Learning Curves
Martin Wistuba, Tejaswini Pedapati
Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many dif…
How can AI Automate End-to-End Data Science?
Charu Aggarwal, Djallel Bouneffouf, Horst Samulowitz +9
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the r…
XferNAS: Transfer Neural Architecture Search
Martin Wistuba
The term Neural Architecture Search (NAS) refers to the automatic optimization of network architectures for a new, previously unknown task. Since testing an architecture is computa…