21 citations · 32 across the 6 of their papers we have counts for
6 papers
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Louis C. Tiao, Vincent Dutordoir, Victor Picheny
Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations. Rec…
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
Henry B. Moss, Sebastian W. Ober, Victor Picheny
Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points sever…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
Henry B. Moss, Sebastian W. Ober, Victor Picheny
Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly pa…
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
Andrei Paleyes, Henry B. Moss, Victor Picheny +2
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit paral…
Increased genetic diversity improves crop yield stability under climate variability: a computational study on sunflower
Pierre Casadebaig, Ronan Trépos, Victor Picheny +3
A crop can be represented as a biotechnical system in which components are either chosen (cultivar, management) or given (soil, climate) and whose combination generates highly vari…