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From Regional to Global: Transfer Learning for Atmospheric Transport Emulators
Jeff Clark, Elena Fillola, Nawid Keshtmand +2
Greenhouse gas emissions estimates can be derived using inverse methods by combining atmospheric concentration observations with chemical transport models. The latter traditionally…
Evaluating Deep Multivariate Imputation Models on Wearable Device Data
Skye Goodman, Roussel Desmond Nzoyem, Leandro Junges +5
Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out together. Deep imputation methods su…
FLEX: Feature Importance from Layered Counterfactual Explanations
Nawid Keshtmand, Roussel Desmond Nzoyem, Jeffrey Nicholas Clark
Machine learning models achieve state-of-the-art performance across domains, yet their lack of interpretability limits safe deployment in high-stakes settings. Counterfactual expla…
Weight-Space Linear Recurrent Neural Networks
Roussel Desmond Nzoyem, Nawid Keshtmand, Enrique Crespo Fernandez +4
We introduce WARP (Weight-space Adaptive Recurrent Prediction), a simple yet powerful model that unifies weight-space learning with linear recurrence to redefine sequence modeling.…
Prototype-enhanced prediction in graph neural networks for climate applications
Nawid Keshtmand, Elena Fillola, Jeffrey Nicholas Clark +2
Data-driven emulators are increasingly being used to learn and emulate physics-based simulations, reducing computational expense and run time. Here, we present a structured way to…
TraCE: Trajectory Counterfactual Explanation Scores
Jeffrey N. Clark, Edward A. Small, Nawid Keshtmand +5
Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box c…
Two-step counterfactual generation for OOD examples
Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry
Two fundamental requirements for the deployment of machine learning models in safety-critical systems are to be able to detect out-of-distribution (OOD) data correctly and to be ab…
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection
Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry
A recent popular approach to out-of-distribution (OOD) detection is based on a self-supervised learning technique referred to as contrastive learning. There are two main variants o…