Jan 2, 2021 · The current work proposes a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that ...

The current work proposes a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple ...

Oct 27, 2020 · A city-level forecast- ing system based on this approach is being used for COVID-19 response in a few highly impacted Indian cities.

Jan 2, 2021 · The current work proposes a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple ...

A novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple application-specific ...

Oct 27, 2020 · ABSTRACT. Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning.

Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning. Existing compartmental and agent-based ...

In the current work, we propose a novel model-agnostic Bayesian optimization approach [3] for learning model parameters from observed data that generalizes to ...

Adaptive COVID-19 Forecasting via Bayesian Optimization. N. Bannur, Harsh ... Forecasting the spread of COVID-19 pandemic in Bangladesh using ARIMA model.

Jan 26, 2021 · The proposed approach is agnostic through the choice of model family and loss function and enables accurate forecasts for fine-grained spatial regions.