OptStream
A number of applications benefit from continuously releasing streams of personal data statistics. The process, however, poses significant privacy risks. Motivated by an application in energy systems, this paper presents OptStream, a novel algorithm for releasing differential private data streams. OptStream is a 4-step procedure consisting of sampling, perturbation, reconstruction, and post-processing modules. The sampling module selects a small set of points to access privately in each period of interest, the perturbation module adds noise to the sampled data points to guarantee privacy, the reconstruction module re-assembles the non-sampling data points from the perturbed sampled points, and the post-processing module uses convex optimization over the private output of the previous modules, as well as the private answers of additional queries on the data stream, to ensure consistency of the data’s salient features. OptStream is used to release a real data stream from the largest transmission operator in Europe. Experimental results show that OptStream not only improves the accuracy of the state-of-the-art by at least one order of magnitude on this application domain, but it is also able to ensure accurate load forecasting based on the private data. …

NIMFA
NIMFA is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring. It supports both dense and sparse matrix representation. NIMFA’s component-based implementation and hierarchical design should help the users to employ already implemented techniques or design and code new strategies for matrix factorization tasks. …

Discriminative PCA (dPCA)
Principal component analysis (PCA) is widely used for feature extraction and dimensionality reduction, with documented merits in diverse tasks involving high-dimensional data. Standard PCA copes with one dataset at a time, but it is challenged when it comes to analyzing multiple datasets jointly. In certain data science settings however, one is often interested in extracting the most discriminative information from one dataset of particular interest (a.k.a. target data) relative to the other(s) (a.k.a. background data). To this end, this paper puts forth a novel approach, termed discriminative (d) PCA, for such discriminative analytics of multiple datasets. Under certain conditions, dPCA is proved to be least-squares optimal in recovering the component vector unique to the target data relative to background data. To account for nonlinear data correlations, (linear) dPCA models for one or multiple background datasets are generalized through kernel-based learning. Interestingly, all dPCA variants admit an analytical solution obtainable with a single (generalized) eigenvalue decomposition. Finally, corroborating dimensionality reduction tests using both synthetic and real datasets are provided to validate the effectiveness of the proposed methods. …

Self-Supervised Semi-Supervised Learning (S^4L)
This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual representation learning. Unifying these two approaches, we propose the framework of self-supervised semi-supervised learning ($S^4L$) and use it to derive two novel semi-supervised image classification methods. We demonstrate the effectiveness of these methods in comparison to both carefully tuned baselines, and existing semi-supervised learning methods. We then show that $S^4L$ and existing semi-supervised methods can be jointly trained, yielding a new state-of-the-art result on semi-supervised ILSVRC-2012 with 10% of labels. …