Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Moss, Henry"'
This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale
Externí odkaz:
http://arxiv.org/abs/2408.16118
Sampling from generative models has become a crucial tool for applications like data synthesis and augmentation. Diffusion, Flow Matching and Continuous Normalizing Flows have shown effectiveness across various modalities, and rely on Gaussian latent
Externí odkaz:
http://arxiv.org/abs/2408.08558
Accurately representing surface weather at the sub-kilometer scale is crucial for optimal decision-making in a wide range of applications. This motivates the use of statistical techniques to provide accurate and calibrated probabilistic predictions a
Externí odkaz:
http://arxiv.org/abs/2405.12614
In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating such black-box objective functions is often the effort required to prepar
Externí odkaz:
http://arxiv.org/abs/2302.11533
Autor:
Picheny, Victor, Berkeley, Joel, Moss, Henry B., Stojic, Hrvoje, Granta, Uri, Ober, Sebastian W., Artemev, Artem, Ghani, Khurram, Goodall, Alexander, Paleyes, Andrei, Vakili, Sattar, Pascual-Diaz, Sergio, Markou, Stratis, Qing, Jixiang, Loka, Nasrulloh R. B. S, Couckuyt, Ivo
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 the plug-and-play of popular TensorFlow-based models within sequential d
Externí odkaz:
http://arxiv.org/abs/2302.08436
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 severely hamper optimisation performance. By exploiting the quality-diversi
Externí odkaz:
http://arxiv.org/abs/2301.10123
Autor:
Griffiths, Ryan-Rhys, Klarner, Leo, Moss, Henry B., Ravuri, Aditya, Truong, Sang, Stanton, Samuel, Tom, Gary, Rankovic, Bojana, Du, Yuanqi, Jamasb, Arian, Deshwal, Aryan, Schwartz, Julius, Tripp, Austin, Kell, Gregory, Frieder, Simon, Bourached, Anthony, Chan, Alex, Moss, Jacob, Guo, Chengzhi, Durholt, Johannes, Chaurasia, Saudamini, Strieth-Kalthoff, Felix, Lee, Alpha A., Cheng, Bingqing, Aspuru-Guzik, Alán, Schwaller, Philippe, Tang, Jian
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending
Externí odkaz:
http://arxiv.org/abs/2212.04450
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with data and the need to run an optimization loop when using a non-Gaussian l
Externí odkaz:
http://arxiv.org/abs/2211.01053
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit parallel processing resources. Multi-Objective Bayesian Optimisation (MOBO)
Externí odkaz:
http://arxiv.org/abs/2206.13326