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Found 12 papers, 8 papers with code
Structured Q-learning For Antibody Design
Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences.
Effects of Safety State Augmentation on Safe Exploration
We further show that Simmer can stabilize training and improve the performance of safe RL with average constraints.
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications.
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
software suite as a black-box oracle to score the target specificity and affinity of designed antibodies \textit{in silico} in an unconstrained fashion~\citep{robert2021one}.
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Ranked #1 on
Molecular Graph Generation
on ZINC
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Bayesian optimisation presents a sample-efficient methodology for global optimisation.
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers.
Ranked #1 on
Hyperparameter Optimization
on Bayesmark
SAMBA: Safe Model-Based & Active Reinforcement Learning
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.
Emergent Communication with World Models
This provides a visual grounding of the message, similar to an enhanced observation of the world, which may include objects outside of the listening agent's field-of-view.
Compositional ADAM: An Adaptive Compositional Solver
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values.
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings
Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data.
Neural Variational Inference For Embedding Knowledge Graphs
While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions.
