3 papers
stat.ML2025
Network inference via process motifs for lagged correlation in linear stochastic processes
Alice C. Schwarze, Sara M. Ichinaga, Bingni W. Brunton
A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in a…
stat.ML2025
Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures
Sara M. Ichinaga, Steven L. Brunton, Aleksandr Y. Aravkin +1
The dynamic mode decomposition (DMD) is a data-driven approach that extracts the dominant features from spatiotemporal data. In this work, we introduce sparse-mode DMD, a new varia…
math.DS2024
Unsupervised multi-scale diagnostics
Karl Lapo, Sara M. Ichinaga, Nathan Kutz
The unsupervised and principled diagnosis of multi-scale data is a fundamental obstacle in modern scientific problems from, for instance, weather and climate prediction, neurology,…