9 citations · 41 across the 15 of their papers we have counts for
8 papers · 1 filter
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…