Speaker: Andrei Pătrașcu (University of Bucharest) Abstract: We continue our adventure by investigating existing results with Robust Principal Component Analysis (RPCA) and its adaptation to existing deep neural networks. Required reading: ZHOU, Chong; PAFFENROTH, Randy C. Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and …

Speaker: Andra Băltoiu (University of Bucharest) Abstract: We continue our investigation on the task of detecting outliers in networks, by looking at the concept of signal variation on a graph. Required reading: A. Sandryhaila and J. M. F. Moura, "Classification via regularization on graphs," 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, …

Speaker: Paul Irofti (University of Bucharest) Abstract: We continue our investigation on the task of detecting outliers in networks when dealing with big-data and investigate existing online and distributed solutions. Required reading: Miao, Xuedan, et al. "Distributed online one-class support vector machine for anomaly detection over networks." IEEE transactions on cybernetics 49.4 (2018): 1475-1488. Liu, …

Speaker: Andrei Pătrașcu (University of Bucharest) Abstract: We continue our adventure by investigating existing results using Gaussian Mixture Models (GMM) for anomaly detection and their adaptation to existing deep neural networks. Required reading: Zong, Bo, et al. "Deep autoencoding gaussian mixture model for unsupervised anomaly detection." (2018). Chapter 11 from Deisenroth, Marc Peter, A. Aldo …

Speaker: Andra Băltoiu (University of Bucharest) Abstract: After attending the "Summer School on Applied Harmonic Analysis and Machine Learning" in Genova,  Andra will give us a short introduction on Optimal Transport in the attempt of finding new prospects for the anomaly detection problem.

Speaker: Marius Popescu (University of Bucharest) Abstract: We continue our adventure by investigating existing natural language processing results that deal with unmasking authorship and their application to anomaly detection. Required reading: M. Koppel, J. Schler, and E. Bonchek-Dokow. Measuring Differentiability: Unmasking Pseudonymous Authors. Journal of Machine Learning Research, 8:1261–1276,  2007. T.R. Ionescu, S. Smeureanu, B. …