21 citations · 54 across the 15 of their papers we have counts for
16 papers
WeatherNext 3: Increasing resolution and performance of global weather models with raw observations
Stephan Rasp, Boris Babenko, Dominic Masters +22
State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spati…
Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya +4
State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts usage for under-r…
Skillful joint probabilistic weather forecasting from marginals
Ferran Alet, Ilan Price, Andrew El-Kadi +8
Machine learning (ML)-based weather models have rapidly risen to prominence due to their greater accuracy and speed than traditional forecasts based on numerical weather prediction…
Translation Equivariant Transformer Neural Processes
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim +5
The effectiveness of neural processes (NPs) in modelling posterior prediction maps -- the mapping from data to posterior predictive distributions -- has significantly improved sinc…
Noise-Aware Differentially Private Regression via Meta-Learning
Ossi Räisä, Stratis Markou, Matthew Ashman +4
Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the go…
Variance-Reducing Couplings for Random Features
Isaac Reid, Stratis Markou, Krzysztof Choromanski +2
Random features (RFs) are a popular technique to scale up kernel methods in machine learning, replacing exact kernel evaluations with stochastic Monte Carlo estimates. They underpi…
Aardvark weather: end-to-end data-driven weather forecasting
Anna Vaughan, Stratis Markou, Will Tebbutt +8
Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning model…
Denoising Diffusion Probabilistic Models in Six Simple Steps
Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou +3
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including imag…
Faster Relative Entropy Coding with Greedy Rejection Coding
Gergely Flamich, Stratis Markou, Jose Miguel Hernandez Lobato
Relative entropy coding (REC) algorithms encode a sample from a target distribution using a proposal distribution using as few bits as possible. Unlike entropy coding, REC…
Autoregressive Conditional Neural Processes
Wessel P. Bruinsma, Stratis Markou, James Requiema +6
Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum like…
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…
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou +8
Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the infor…
Notes on the runtime of A* sampling
Stratis Markou
The challenge of simulating random variables is a central problem in Statistics and Machine Learning. Given a tractable proposal distribution , from which we can draw exact samp…
Practical Conditional Neural Processes Via Tractable Dependent Predictions
Stratis Markou, James Requeima, Wessel P. Bruinsma +2
Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and natur…
Fast Relative Entropy Coding with A* coding
Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
Relative entropy coding (REC) algorithms encode a sample from a target distribution using a proposal distribution , such that the expected codelength is $\mathcal{O}(D_{KL}[…
Efficient Gaussian Neural Processes for Regression
Stratis Markou, James Requeima, Wessel Bruinsma +1
Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attractive family of meta-learning models which produce well-calibrated predictions, enable fast inference at test t…