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Simon Mak

3 accepted papers

2026

AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation

AAAI 2026technical

Hierarchical representations provide powerful and principled approaches for analyzing many musical genres. Such representations have been broadly studied in music theory, for instance via Schenkerian analysis (SchA). Hierarchical music analyses, however, are highly cost-intensive; the analysis of a

Cited by 0SourcePDFScholar
2024

Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression

AISTATS 2024poster

Fourier feature approximations have been successfully applied in the literature for scalable Gaussian Process (GP) regression. In particular, Quadrature Fourier Features (QFF) derived from Gaussian quadrature rules have gained popularity in recent years due to their improved approximation accuracy a…

2020

Uncertainty Quantification for Inferring Hawkes Networks

NeurIPS 2020poster

Multivariate Hawkes processes are commonly used to model streaming networked event data in a wide variety of applications. However, it remains a challenge to extract reliable inference from complex datasets with uncertainty quantification. Aiming towards this, we develop a statistical inference fram…

Cited by 14SourcePDFScholar