91 citations · 526 across the 50 of their papers we have counts for
42 papers · 1 filter
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Jannis Becktepe, Julian Dierkes, Carolin Benjamins +7
Hyperparameters are a critical factor in reliably training well-performing reinforcement learning (RL) agents. Unfortunately, developing and evaluating automated approaches for tun…
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj +3
Dynamic Algorithm Configuration (DAC) addresses the challenge of dynamically setting hyperparameters of an algorithm for a diverse set of instances rather than focusing solely on i…
Position: A Call to Action for a Human-Centered AutoML Paradigm
Marius Lindauer, Florian Karl, Anne Klier +6
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research o…
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +2
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is hig…
Learning Activation Functions for Sparse Neural Networks
Mohammad Loni, Aditya Mohan, Mehdi Asadi +1
Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accu…
Hyperparameters in Reinforcement Learning and How To Tune Them
Theresa Eimer, Marius Lindauer, Roberta Raileanu
In order to improve reproducibility, deep reinforcement learning (RL) has been adopting better scientific practices such as standardized evaluation metrics and reporting. However,…
AutoML in Heavily Constrained Applications
Felix Neutatz, Marius Lindauer, Ziawasch Abedjan
Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hype…
Structure in Deep Reinforcement Learning: A Survey and Open Problems
Aditya Mohan, Amy Zhang, Marius Lindauer
Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous a…
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Neeratyoy Mallik, Edward Bergman, Carl Hvarfner +5
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been develo…
Automated Machine Learning for Remaining Useful Life Predictions
Marc-André Zöller, Fabian Mauthe, Peter Zeiler +2
Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL pr…
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
Alexander Tornede, Difan Deng, Theresa Eimer +8
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language…
Hyperparameters in Contextual RL are Highly Situational
Theresa Eimer, Carolin Benjamins, Marius Lindauer
Although Reinforcement Learning (RL) has shown impressive results in games and simulation, real-world application of RL suffers from its instability under changing environment cond…
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
Carolin Benjamins, Anja Jankovic, Elena Raponi +3
Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problem…
PI is back! Switching Acquisition Functions in Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +4
Bayesian Optimization (BO) is a powerful, sample-efficient technique to optimize expensive-to-evaluate functions. Each of the BO components, such as the surrogate model, the acquis…
Towards Meta-learned Algorithm Selection using Implicit Fidelity Information
Aditya Mohan, Tim Ruhkopf, Marius Lindauer
Automatically selecting the best performing algorithm for a given dataset or ranking multiple algorithms by their expected performance supports users in developing new machine lear…
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
René Sass, Eddie Bergman, André Biedenkapp +2
Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pip…
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution
Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer +1
Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision p…
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
Frederik Schubert, Carolin Benjamins, Sebastian Döhler +2
The goal of Unsupervised Reinforcement Learning (URL) is to find a reward-agnostic prior policy on a task domain, such that the sample-efficiency on supervised downstream tasks is…
Efficient Automated Deep Learning for Time Series Forecasting
Difan Deng, Florian Karl, Frank Hutter +2
Recent years have witnessed tremendously improved efficiency of Automated Machine Learning (AutoML), especially Automated Deep Learning (AutoDL) systems, but recent work focuses on…
BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Carl Hvarfner, Danny Stoll, Artur Souza +3
Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sampl…
Practitioner Motives to Use Different Hyperparameter Optimization Methods
Niclas Kannengießer, Niklas Hasebrook, Felix Morsbach +5
Programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization and evolutionary algorithms, are highly sample-efficient in identifying optimal hyperparameter…
Contextualize Me -- The Case for Context in Reinforcement Learning
Carolin Benjamins, Theresa Eimer, Frederik Schubert +6
While Reinforcement Learning ( RL) has made great strides towards solving increasingly complicated problems, many algorithms are still brittle to even slight environmental changes.…
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Zhengying Liu, Adrien Pavao, Zhen Xu +22
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
Jack Parker-Holder, Raghu Rajan, Xingyou Song +9
The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable a…