Anomaly Detection Reading Group: Gaussian Mixture Models

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 Faisal, and Cheng Soon Ong. “Mathematics for Machine Learning.” (2018).

Anomaly Detection Reading Group: Distributed Online AD

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, Zhaoting, Ying Liu, and Chunguang Li. “Distributed sparse recursive least-squares over networks.” IEEE Transactions on Signal Processing 62.6 (2014): 1386-1395.

Anomaly Detection Reading Group: Graph Classification

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, 2013, pp. 495-498.

S. Chen, A. Sandryhaila, J. M. F. Moura and J. Kovačević, “Signal Recovery on Graphs: Variation Minimization,” in IEEE Transactions on Signal Processing, vol. 63, no. 17, pp. 4609-4624, Sept.1, 2015.

Anomaly Detection Reading Group: Deep RPCA

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 Data Mining. ACM, 2017. p. 665-674.

CANDÈS, Emmanuel J., et al. Robust principal component analysis?. Journal of the ACM (JACM), 2011, 58.3: 11.

Anomaly Detection Reading Group: Deep OC-SVM

Speaker: Andrei Pătrașcu (University of Bucharest)


Abstract: Recent empirical results confirm that one-class (OC) classification methods remain among the most important learning strategies for anomaly detection. In this seminar, we will technically describe in detail multiple basic OC schemes such as OC-SVM and SVDD and their deep variants, in order to identify room of improvements or generalization directions towards the graph anomaly detection context.

Curs optional Secureworks

Firma Secureworks a propus un curs optional pentru anul II, semestrul I. Puteti consulta fisa cursului aici. Recomandam studentilor SLA care finalizeaza anul I sa aleaga acest curs atunci cand vor opta pentru cursurile optionale din anul II. Pentru detalii privind alegerea cursurilor optionale, urmariti anunturile FMI sau intrebati la secretariat.