108 citations · 112 across the 4 of their papers we have counts for
5 papers
Falsehoods that ML researchers believe about OOD detection
Andi Zhang, Damon Wischik
An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD. But…
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation
Raghul Parthipan, Damon J. Wischik
How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time? Typically, the training data comprises co…
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking +1
The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through paramet…
Disentangling Domain and Content
Dan Andrei Iliescu, Aliaksei Mikhailiuk, Damon Wischik +1
Many real-world datasets can be divided into groups according to certain salient features (e.g. grouping images by subject, grouping text by font, etc.). Often, machine learning ta…
Switched networks with maximum weight policies: Fluid approximation and multiplicative state space collapse
Devavrat Shah, Damon Wischik
We consider a queueing network in which there are constraints on which queues may be served simultaneously; such networks may be used to model input-queued switches and wireless ne…