17 citations · 57 across the 10 of their papers we have counts for
8 papers · 1 filter
Structured Q-learning For Antibody Design
Alexander I. Cowen-Rivers, Philip John Gorinski, Aivar Sootla +5
Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences. For example, one of the crucial steps involved in antibody desi…
Effects of Safety State Augmentation on Safe Exploration
Aivar Sootla, Alexander I. Cowen-Rivers, Jun Wang +1
Safe exploration is a challenging and important problem in model-free reinforcement learning (RL). Often the safety cost is sparse and unknown, which unavoidably leads to constrain…
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Aivar Sootla, Alexander I. Cowen-Rivers, Taher Jafferjee +4
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications. For example, plan…
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval +9
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By…
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov +3
Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of th…
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov +8
In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedastic…