ML Nuremberg

AutoRL-Bench

DeepPipe

[KDD 2023] Deep Pipeline Embeddings for AutoML

DeepRankingEnsembles

[ICLR 2023] Deep Ranking Ensembles for Hyperparameter Optimization

DPL

[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.

DyHPO

[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations

FSBO

[ICLR 2021] Few Shot Bayesian Optimization

HPO-B

[NeurIPS DBT 2021] HPO-B

QuickTune

[ICLR2024] Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How

Revisiting-MLPs

WellTunedSimpleNets

[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets

machinelearningnuremberg/WellTunedSimpleNets

[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets

machinelearningnuremberg/HPO-B

[NeurIPS DBT 2021] HPO-B

machinelearningnuremberg/QuickTune

[ICLR2024] Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How

machinelearningnuremberg/DeepPipe

[KDD 2023] Deep Pipeline Embeddings for AutoML

machinelearningnuremberg/FSBO

[ICLR 2021] Few Shot Bayesian Optimization

machinelearningnuremberg/DeepRankingEnsembles

[ICLR 2023] Deep Ranking Ensembles for Hyperparameter Optimization

machinelearningnuremberg/DPL

[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.

machinelearningnuremberg/DyHPO

[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations

machinelearningnuremberg/Revisiting-MLPs

machinelearningnuremberg/AutoRL-Bench

machinelearningnuremberg/HPO-RL-Bench

machinelearningnuremberg/INN

Explainable deep networks that are not only as accurate as their black-box deep-learning counterparts but also as interpretable as state-of-the-art explanation techniques.

machinelearningnuremberg/RCGP

machinelearningnuremberg/HPO4DL

machinelearningnuremberg/Pipeline-Bench

machinelearningnuremberg/releaunifreiburg.github.io