Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement Learning

1 code implementation • 15 Apr 2024 • Tidiane Camaret Ndir, André Biedenkapp, Noor Awad

In this work, we address the challenge of zero-shot generalization (ZSG) in Reinforcement Learning (RL), where agents must adapt to entirely novel environments without additional training.

reinforcement-learning Reinforcement Learning (RL) +1

MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization

no code implementations • 8 May 2023 • Noor Awad, Ayushi Sharma, Philipp Muller, Janek Thomas, Frank Hutter

Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models.

Fairness Hyperparameter Optimization +1

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML

no code implementations • 15 Mar 2023 • Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter

The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices.

AutoML Fairness

Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator

1 code implementation • 13 Dec 2022 • Shuhei Watanabe, Noor Awad, Masaki Onishi, Frank Hutter

Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL).

Fairness Hyperparameter Optimization +1

Automated Dynamic Algorithm Configuration

1 code implementation • 27 May 2022 • Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor Awad, Theresa Eimer, Marius Lindauer, Frank Hutter

The performance of an algorithm often critically depends on its parameter configuration.

HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO

2 code implementations • 14 Sep 2021 • Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, Frank Hutter

To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications.

Hyperparameter Optimization

DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization

2 code implementations • 20 May 2021 • Noor Awad, Neeratyoy Mallik, Frank Hutter

Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important than ever.

Hyperparameter Optimization Neural Architecture Search

Squirrel: A Switching Hyperparameter Optimizer

1 code implementation • 15 Dec 2020 • Noor Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, Andre' Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter

In this short note, we describe our submission to the NeurIPS 2020 BBO challenge.

AutoML

Differential Evolution for Neural Architecture Search

1 code implementation • 11 Dec 2020 • Noor Awad, Neeratyoy Mallik, Frank Hutter

Neural architecture search (NAS) methods rely on a search strategy for deciding which architectures to evaluate next and a performance estimation strategy for assessing their performance (e. g., using full evaluations, multi-fidelity evaluations, or the one-shot model).

Bayesian Optimization Neural Architecture Search

Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control

no code implementations • 28 Oct 2019 • Jörg K. H. Franke, Gregor Köhler, Noor Awad, Frank Hutter

Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures.

Continuous Control reinforcement-learning +1