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Predicting infections in the Covid-19 pandemic -- lessons learned
Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the public policy and non-pharmaceutical interventions.
Privacy-Preserving Learning of Human Activity Predictors in Smart Environments
The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior.
Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications
The approach is based on the concept of {\em self-rank}, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation.
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation.
