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William Herlands

2 accepted papers

2018

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data

AISTATS 2018poster

Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Addit…

Cited by 0SourcePDFScholar
2016

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

AISTATS 2016poster

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to flexibly define a change surface in combination with expressive spectral…

Cited by 39SourcePDFScholar