Ritabrata Dutta - Probabilistic Forecasting with Conditional Generative Networks via Scoring Rule Minimization
|Starts:||14:00 16 Feb 2022|
|Ends:||15:00 16 Feb 2022|
|What is it:||Seminar|
|Organiser:||Department of Mathematics|
|Who is it for:||University staff, External researchers, Current University students|
Ritabrata Dutta, Assistant Professor of Statistics at the Department of Statistics, University of Warwick is our speaker for the Statistics seminar series.
Title: Probabilistic Forecasting with Conditional Generative Networks via Scoring Rule Minimization
Abstract: Probabilistic forecasting consists of stating a probability distribution for a future outcome based on past observations. In meteorology, ensembles of physics-based numerical models are run to get such distribution. Usually, performance is evaluated with scoring rules, functions of the forecast distribution and the observed outcome. With some scoring rules, calibration and sharpness of the forecast can be assessed at the same time.
In deep learning, generative neural networks parametrize distributions on high-dimensional spaces and easily allow sampling by transforming draws from a latent variable. Conditional generative networks additionally constrain the distribution on an input variable. In this manuscript, we perform probabilistic forecasting with conditional generative networks trained to minimize scoring rule values. In contrast to Generative Adversarial Networks (GANs), no discriminator is required and training is stable. We perform experiments on two chaotic models and a global dataset of weather observations; results are satisfactory and better calibrated than what achieved by GANs.
Organisation: University of Warwick
Travel and Contact Information
Zoom link: https://zoom.us/j/92947173491