84 citations · 89 across the 6 of their papers we have counts for
6 papers
Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields
Kim Bente, Roman Marchant, Fabio Ramos
To reliably project future sea level rise, ice sheet models require inputs that respect physics. Embedding physical principles like mass conservation into models that interpolate A…
A Case Study in Model Failure? COVID-19 Daily Deaths and ICU Bed Utilisation Predictions in New York State
Vincent Chin, Noelle I. Samia, Roman Marchant +4
Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we di…
Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions
Roman Marchant, Noelle I. Samia, Ori Rosen +2
This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for p…
Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series
Nick James, Roman Marchant, Richard Gerlach +1
Discrimination between non-stationarity and long-range dependency is a difficult and long-standing issue in modelling financial time series. This paper uses an adaptive spectral te…
Sequential Bayesian Optimisation as a POMDP for Environment Monitoring with UAVs
Philippe Morere, Roman Marchant, Fabio Ramos
Bayesian Optimisation has gained much popularity lately, as a global optimisation technique for functions that are expensive to evaluate or unknown a priori. While classical BO foc…
Occupancy Map Building through Bayesian Exploration
Gilad Francis, Lionel Ott, Roman Marchant +1
We propose a novel holistic approach for safe autonomous exploration and map building based on constrained Bayesian optimisation. This method finds optimal continuous paths instead…