no code implementations • 9 Feb 2023 • Roberto Vega, Zehra Shah, Pouria Ramazi, Russell Greiner
Here, we propose two new methods to model the number of people infected with COVID-19 over time, each as a linear combination of latent sub-populations -- i. e., when we do not know which person is in which sub-population, and the only available observations are the aggregates across all sub-populations.
no code implementations • 15 Sep 2022 • Gholamali Aminian, Armin Behnamnia, Roberto Vega, Laura Toni, Chengchun Shi, Hamid R. Rabiee, Omar Rivasplata, Miguel R. D. Rodrigues
We propose learning methods for problems where feedback is missing for some samples, so there are samples with feedback and samples missing-feedback in the logged data.
1 code implementation • 14 Jan 2022 • Roberto Vega, Russell Greiner
A predictor, $f_A : X \to Y$, learned with data from a source domain (A) might not be accurate on a target domain (B) when their distributions are different.
no code implementations • 3 Jun 2021 • 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.
no code implementations • 11 Feb 2021 • Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner
This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations.