For what purpose was the dataset created? The external census dataset used in curation was created for census. Who created the dataset, and on behalf of which entity? Who funded the creation of the dataset? The creation of the curated dataset was funded by ETH Zurich.

We used reliable public data from 1970 to 2020 to curate attributes like prior criminal counts and recidivism outcomes. The dataset contains large number of samples from five racial groups, in addition to information like sex and age (at judgment and first offense).

Recent breakthroughs in deep neural networks (DNNs) have motivated an explosive demand for intelligent edge devices. Many of them, such as autonomous vehicles and healthcare wearables, require real-time andon-site learning toenable them toproactivelylearn from newdataandadapt todynamic environments.

As the state-of-the-art framework, i.e., RecEraser, naturally achieves full unlearning completeness,

We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes.

However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods.