Master’s Thesis Presentation • Machine Learning • Multivariate Triangular Quantile Maps for Novelty Detection

Please note: This master’s thesis presentation will take place in DC 3317 and online.

Jingjing Wang, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Yaoliang Yu

Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches. In this thesis, we present a general framework for neural novelty detection that centers around a multivariate extension of the univariate quantile function. Our general framework unifies and extends many classical and recent novelty detection algorithms, and opens the way to exploit recent advances in flow-based neural density estimation. We adapt the multiple gradient descent algorithm to obtain the first efficient end-to-end implementation of our framework that is free of tuning hyperparameters. Extensive experiments over a number of real datasets confirm the efficacy of our proposed method against state-of-the-art alternatives.

To attend this master’s thesis presentation in person, please go to DC 3317. You can also attend virtually using Zoom at https://uwaterloo.zoom.us/j/91069612729.