We are pleased to announce that the Annual Review of Statistics and Its Application (ARSIA) is publishing the article Manifold Learning: What, How, and Why, co-authored by Marina Meila, Professor of Statistics, and Hanyu Zhang, Statistics Ph.D. graduate. The expected publication date is March 2024.
The Annual Review of Statistics and Its Application publishes in-depth overview papers about major methodological advances and the computational tools that allow for their implementation.
This review presents the underlying principles of ML, its representative methods, and their statistical foundations, all from a practicing statistician’s perspective. It describes the trade-offs and what theory tells us about the parameter and algorithmic choices that lead to reliable inferences.