Search Results for author: Lamiae Azizi

Found 6 papers, 4 papers with code

Multilayer Networks for Text Analysis with Multiple Data Types

1 code implementation30 Jun 2021 Charles C. Hyland, Yuanming Tao, Lamiae Azizi, Martin Gerlach, Tiago P. Peixoto, Eduardo G. Altmann

We are interested in the widespread problem of clustering documents and finding topics in large collections of written documents in the presence of metadata and hyperlinks.

Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes

no code implementations16 Jun 2020 Simon Luo, Feng Zhou, Lamiae Azizi, Mahito Sugiyama

We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower dimensional projections.

Additive models

Time-varying neural network for stock return prediction

1 code implementation5 Mar 2020 Steven Y. K. Wong, Jennifer Chan, Lamiae Azizi, Richard Y. D. Xu

We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics.

Novel semi-metrics for multivariate change point analysis and anomaly detection

no code implementations4 Nov 2019 Nick James, Max Menzies, Lamiae Azizi, Jennifer Chan

This paper proposes a new method for determining similarity and anomalies between time series, most practically effective in large collections of (likely related) time series, by measuring distances between structural breaks within such a collection.

Anomaly Detection Time Series

Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation

1 code implementation25 Sep 2019 Simon Luo, Lamiae Azizi, Mahito Sugiyama

We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS).

Time Series

Variational Nonparametric Discriminant Analysis

1 code implementation10 Dec 2018 Weichang Yu, Lamiae Azizi, John T. Ormerod

Variable selection and classification methods are common objectives in the analysis of high-dimensional data.


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