6.4k citations · 14.8k across the 73 of their papers we have counts for
4 papers · 2 filters
Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries
Solt Kovács, Housen Li, Lorenz Haubner +2
Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching through all candidates requi…
Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
Solt Kovács, Housen Li, Peter Bühlmann
In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives.…
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding
Zijian Guo, Domagoj Ćevid, Peter Bühlmann
Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding. We focus on a high-dimensional linear regr…
Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
Solt Kovács, Housen Li, Peter Bühlmann +1
In recent years, there has been an increasing demand on efficient algorithms for large scale change point detection problems. To this end, we propose seeded binary segmentation, an…