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Poster
Multivariate Triangular Quantile Maps for Novelty Detection
Jingjing Wang · Sun Sun · Yaoliang Yu

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #56
in Algorithms -- Unsupervised Learning »

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 work, we present a general framework for neural novelty detection that centers around a multivariate extension of the univariate quantile function. Our 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.

Keywords: Unsupervised Learning · Algorithms · Algorithms -> Density Estimation; Deep Learning -> Deep Autoencoders; Deep Learning · Generative Models; Probabilistic Methods
[ Paper]  [ Poster] 

Author Information

Jingjing Wang (University of Waterloo)
Sun Sun (National Research Council)
Yaoliang Yu (University of Waterloo)

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