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05/18/2023
Physics Inspired Approaches Towards Understanding Gaussian Processes
Prior beliefs about the latent function to shape inductive biases can be...
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08/11/2022
A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets
Multifidelity and multioutput optimisation algorithms are of active inte...
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05/03/2022
Self-focusing virtual screening with active design space pruning
High-throughput virtual screening is an indispensable technique utilized...
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05/11/2020
Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19
The novel Wuhan coronavirus known as SARS-CoV-2 has brought almost unpre...
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01/28/2020
Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption
Much of machine learning relies on the use of large amounts of data to t...
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11/08/2019
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning
Reinforcement Learning (RL) has demonstrated state-of-the-art results in...
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09/17/2018
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
Chemical representations derived from deep learning are emerging as a po...
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07/03/2018
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization
Bayesian optimization offers the possibility of optimizing black-box ope...
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06/04/2018
Efficient and Scalable Batch Bayesian Optimization Using K-Means
We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch sa...
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06/06/2017
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space
Chemical space is so large that brute force searches for new interesting...
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08/19/2016