8 citations · 20 across the 11 of their papers we have counts for
11 papers
Optimal scenario design for climate emulation
Christopher B. Womack, Shahine Bouabid, Andrei Sokolov +4
As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical con…
Score-based generative emulation of impact-relevant Earth system model outputs
Shahine Bouabid, Andre Nogueira Souza, Raffaele Ferrari
Policy targets evolve faster than the Coupled Model Intercomparison Project cycles, complicating adaptation and mitigation planning that must often contend with outdated projection…
Indirect Query Bayesian Optimization with Integrated Feedback
Mengyan Zhang, Shahine Bouabid, Cheng Soon Ong +2
We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expe…
Domain Generalisation via Imprecise Learning
Anurag Singh, Siu Lun Chau, Shahine Bouabid +1
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.,…
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation
Shahine Bouabid, Dino Sejdinovic, Duncan Watson-Parris
Emulators, or reduced complexity climate models, are surrogate Earth system models that produce projections of key climate quantities with minimal computational resources. Using ti…
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
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. We show that the independences arising from the pr…
AODisaggregation: toward global aerosol vertical profiles
Shahine Bouabid, Duncan Watson-Parris, Sofija Stefanović +2
Aerosol-cloud interactions constitute the largest source of uncertainty in assessments of the anthropogenic climate change. This uncertainty arises in part from the difficulty in m…
Deconditional Downscaling with Gaussian Processes
Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
Refining low-resolution (LR) spatial fields with high-resolution (HR) information, often known as statistical downscaling, is challenging as the diversity of spatial datasets often…
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
Paula Harder, William Jones, Redouane Lguensat +8
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. The gap can be…
Predicting Landsat Reflectance with Deep Generative Fusion
Shahine Bouabid, Maxim Chernetskiy, Maxime Rischard +1
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. T…
Mixup Regularization for Region Proposal based Object Detectors
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 generaliza…