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Found 7 papers, 2 papers with code
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science.
Linear combinations of latents in diffusion models: interpolation and beyond
We propose Combination of Gaussian variables (COG), a novel interpolation method that addresses this, is easy to implement yet matches or improves upon current methods.
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks
Accurately representing surface weather at the sub-kilometer scale is crucial for optimal decision-making in a wide range of applications.
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning.
Scalable Thompson Sampling using Sparse Gaussian Process Models
We provide theoretical guarantees and show that the drastic reduction in computational complexity of scalable TS can be enjoyed without loss in the regret performance over the standard TS.
Bayesian Quantile and Expectile Optimisation
In this paper, we propose new variational models for Bayesian quantile and expectile regression that are well-suited for heteroscedastic noise settings.
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP Models
K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning.
