no code implementations • 22 Feb 2024 • Steven Y. K. Wong, Jennifer S. K. Chan, Lamiae Azizi
Time-series with time-varying variance pose a unique challenge to uncertainty quantification (UQ) methods.
no code implementations • 29 Sep 2021 • 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 Poisson processes using projections into lower-dimensional space.
1 code implementation • 30 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.
no code implementations • NeurIPS Workshop DL-IG 2020 • Simon Luo, Feng Zhou, Lamiae Azizi, Mahito Sugiyama
Learning of the model is achieved via convex optimization, thanks to the dually flat statistical manifold generated by the log-linear model.
no code implementations • 16 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.
1 code implementation • 5 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.
no code implementations • 4 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.
1 code implementation • 25 Sep 2019 • Simon Luo, Lamiae Azizi, Mahito Sugiyama
We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS).
1 code implementation • 10 Dec 2018 • Weichang Yu, Lamiae Azizi, John T. Ormerod
Variable selection and classification methods are common objectives in the analysis of high-dimensional data.
Methodology