Guaranteeing a certain level of user privacy in an arbitrary piece of te...
Accurately learning from user data while providing quantifiable privacy
...
This paper describes HyperStream, a large-scale, flexible and robust sof...
Differential privacy is a mathematical framework for privacy-preserving ...
We are concerned with obtaining well-calibrated output distributions fro...
Continual learning aims to enable machine learning models to learn a gen...
Active learning holds promise of significantly reducing data annotation ...
This paper describes a reference architecture for self-maintaining syste...
Item Response Theory (IRT) aims to assess latent abilities of respondent...
There is a widely-accepted need to revise current forms of health-care
p...
We present a derivation of the Kullback Leibler (KL)-Divergence (also kn...