63 citations · 244 across the 40 of their papers we have counts for
7 papers · 1 filter
Transformers Can Do Bayesian Inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango +2
Currently, it is hard to reap the benefits of deep learning for Bayesian methods, which allow the explicit specification of prior knowledge and accurately capture model uncertainty…
Multi-task problems are not multi-objective
Michael Ruchte, Josif Grabocka
Multi-objective optimization (MOO) aims at finding a set of optimal configurations for a given set of objectives. A recent line of work applies MOO methods to the typical Machine L…
Well-tuned Simple Nets Excel on Tabular Datasets
Arlind Kadra, Marius Lindauer, Frank Hutter +1
Tabular datasets are the last "unconquered castle" for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent…
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…
Scalable Pareto Front Approximation for Deep Multi-Objective Learning
Michael Ruchte, Josif Grabocka
Multi-objective optimization (MOO) is a prevalent challenge for Deep Learning, however, there exists no scalable MOO solution for truly deep neural networks. Prior work either dema…
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…