19 citations · 80 across the 14 of their papers we have counts for
14 papers
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…
Learning Geometric Constraints in Task and Motion Planning
Tianyu Ren, Alexander Imani Cowen-Rivers, Haitham Bou Ammar +1
Searching for bindings of geometric parameters in task and motion planning (TAMP) is a finite-horizon stochastic planning problem with high-dimensional decision spaces. A robot man…
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit +12
Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody domi…
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…
SAMBA: Safe Model-Based & Active Reinforcement Learning
Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens +4
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our metho…
Emergent Communication with World Models
Alexander I. Cowen-Rivers, Jason Naradowsky
We introduce Language World Models, a class of language-conditional generative model which interpret natural language messages by predicting latent codes of future observations. Th…
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings
Alexander I. Cowen-Rivers, Pasquale Minervini, Tim Rocktaschel +3
Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. While traditional varia…
Infer Your Enemies and Know Yourself, Learning in Real-Time Bidding with Partially Observable Opponents
Manxing Du, Alexander I. Cowen-Rivers, Ying Wen +4
Real-time bidding, as one of the most popular mechanisms for selling online ad slots, facilitates advertisers to reach their potential customers. The goal of bidding optimization i…
Towards Incremental Cylindrical Algebraic Decomposition in Maple
Alexander Imani Cowen-Rivers, Matthew England
Cylindrical Algebraic Decomposition (CAD) is an important tool within computational real algebraic geometry, capable of solving many problems for polynomial systems over the reals.…
Summer Research Report: Towards Incremental Lazard Cylindrical Algebraic Decomposition
Alexander I. Cowen-Rivers, Matthew England
Cylindrical Algebraic Decomposition (CAD) is an important tool within computational real algebraic geometry, capable of solving many problems to do with polynomial systems over the…