1 code implementation • 6 Apr 2024 • Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
Out-of-distribution (OOD) generalisation is challenging because it involves not only learning from empirical data, but also deciding among various notions of generalisation, e. g., optimising the average-case risk, worst-case risk, or interpolations thereof.
1 code implementation • 14 Jul 2023 • Shahine Bouabid, Dino Sejdinovic, Duncan Watson-Parris
The result is an emulator that \textit{(i)} enjoys the flexibility of statistical machine learning models and can learn from data, and \textit{(ii)} has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system.
1 code implementation • 26 Jan 2023 • Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
A directed acyclic graph (DAG) provides valuable prior knowledge that is often discarded in regression tasks in machine learning.
1 code implementation • 6 May 2022 • Shahine Bouabid, Duncan Watson-Parris, Sofija Stefanović, Athanasios Nenes, Dino Sejdinovic
In this work, we develop a framework for the vertical disaggregation of AOD into extinction profiles, i. e. the measure of light extinction throughout an atmospheric column, using readily available vertically resolved meteorological predictors such as temperature, pressure or relative humidity.
1 code implementation • NeurIPS 2021 • Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
Yet, when LR samples are modeled as aggregate conditional means of HR samples with respect to a mediating variable that is globally observed, the recovery of the underlying fine-grained field can be framed as taking an "inverse" of the conditional expectation, namely a deconditioning problem.
1 code implementation • 13 Nov 2020 • Paula Harder, William Jones, Redouane Lguensat, Shahine Bouabid, James Fulton, Dánell Quesada-Chacón, Aris Marcolongo, Sofija Stefanović, Yuhan Rao, Peter Manshausen, Duncan Watson-Parris
The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night.
1 code implementation • 9 Nov 2020 • Shahine Bouabid, Maxim Chernetskiy, Maxime Rischard, Jevgenij Gamper
Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution as no single sensor provides fine-grained acquisitions with frequent coverage.
no code implementations • 4 Mar 2020 • Shahine Bouabid, Vincent Delaitre
Mixup - a neural network regularization technique based on linear interpolation of labeled sample pairs - has stood out by its capacity to improve model's robustness and generalizability through a surprisingly simple formalism.