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Found 5 papers, 1 papers with code
Modeling and Forecasting COVID-19 Cases using Latent Subpopulations
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.
Semi-supervised Batch Learning From Logged Data
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.
Domain-shift adaptation via linear transformations
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.
SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting
Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions.
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations.
