31 citations · 89 across the 11 of their papers we have counts for
12 papers
Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs
Mohamed Mouhajir, Limei Wang, El Houcine Bergou +3
Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially refl…
Quantifying neural network uncertainty under volatility clustering
Steven Y. K. Wong, Jennifer S. K. Chan, Lamiae Azizi
Time-series with volatility clustering pose a unique challenge to uncertainty quantification (UQ) for returns forecasts. Methods for UQ such as Deep Evidential regression offer a s…
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data
W Yu, S Wade, H D Bondell +1
High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology,…
Multilayer Networks for Text Analysis with Multiple Data Types
Charles C. Hyland, Yuanming Tao, Lamiae Azizi +3
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. To tack…
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes
Simon Luo, Feng Zhou, Lamiae Azizi +1
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…
Time-varying neural network for stock return prediction
Steven Y. K. Wong, Jennifer Chan, Lamiae Azizi +1
We consider the problem of neural network training in a time-varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems…
Novel semi-metrics for multivariate change point analysis and anomaly detection
Nick James, Max Menzies, Lamiae Azizi +1
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 m…
Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation
Simon Luo, Lamiae Azizi, Mahito Sugiyama
We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS). Our formulation is based on the log-linear model equipped wit…
Micro, Meso, Macro: the effect of triangles on communities in networks
Sophie Wharrie, Lamiae Azizi, Eduardo G. Altmann
Meso-scale structures (communities) are used to understand the macro-scale properties of complex networks, such as their functionality and formation mechanisms. Micro-scale structu…
Variational Nonparametric Discriminant Analysis
Weichang Yu, Lamiae Azizi, John T. Ormerod
Variable selection and classification are common objectives in the analysis of high-dimensional data. Most such methods make distributional assumptions that may not be compatible w…
Spatial risk mapping for rare disease with hidden Markov fields and variational EM
Florence Forbes, Myriam Charras-Garrido, Lamiae Azizi +2
Current risk mapping models for pooled data focus on the estimated risk for each geographical unit. A risk classification, that is, grouping of geographical units with similar risk…
PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes
Silvia Liverani, David I. Hastie, Lamiae Azizi +2
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically lin…