885 citations · 3.3k across the 27 of their papers we have counts for
32 papers
The Shift Toward Open and Reproducible AI Research
Kevin L Coakley, Thijs Snelleman, Holger Hoos +1
The reproducibility crisis has directed the AI research community toward improving documentation practices. Several studies have identified methodological issues, and in response,…
Automated Reproducibility Has a Problem Statement Problem
Thijs Snelleman, Peter Lundestad Lawrence, Holger H. Hoos +1
Background. Reproducibility is essential to the scientific method, but reproduction is often a laborious task. Recent works have attempted to automate this process and relieve rese…
Towards General Negotiation Strategies with End-to-End Reinforcement Learning
Bram M. Renting, Thomas M. Moerland, Holger H. Hoos +1
The research field of automated negotiation has a long history of designing agents that can negotiate with other agents. Such negotiation strategies are traditionally based on manu…
Combining Automated Optimisation of Hyperparameters and Reward Shape
Julian Dierkes, Emma Cramer, Holger H. Hoos +1
There has been significant progress in deep reinforcement learning (RL) in recent years. Nevertheless, finding suitable hyperparameter configurations and reward functions remains c…
Competitions in AI -- Robustly Ranking Solvers Using Statistical Resampling
Chris Fawcett, Mauro Vallati, Holger H. Hoos +1
Solver competitions play a prominent role in assessing and advancing the state of the art for solving many problems in AI and beyond. Notably, in many areas of AI, competitions hav…
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Lennart Purucker, Lennart Schneider, Marie Anastacio +3
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe…