2 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
Modeling and Forecasting COVID-19 Cases using Latent Subpopulations
Roberto Vega, Zehra Shah, Pouria Ramazi +1
Classical epidemiological models assume homogeneous populations. There have been important extensions to model heterogeneous populations, when the identity of the sub-populations i…
Semi-supervised Batch Learning From Logged Data
Gholamali Aminian, Armin Behnamnia, Roberto Vega +5
Off-policy learning methods are intended to learn a policy from logged data, which includes context, action, and feedback (cost or reward) for each sample point. In this work, we b…
Domain-shift adaptation via linear transformations
Roberto Vega, Russell Greiner
A predictor, , learned with data from a source domain (A) might not be accurate on a target domain (B) when their distributions are different. Domain adaptation aims…
SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting
Roberto Vega, Leonardo Flores, Russell Greiner
Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR…