Michael Hanselmann
Papers
1
Total Citations
3
H-Index
1
About
Michael Hanselmann is a researcher whose work lies at the intersection of machine learning and probabilistic modeling, with a particular focus on Gaussian process regression. His most notable contribution is the development of a fast, greedy algorithm for insertion and deletion in sparse Gaussian process regression, introduced in his 2015 paper. This work provides a computationally efficient criterion for selecting training points, building on foundational ideas by Smola and Bartlett, and offers a practical solution for scaling Gaussian processes to larger datasets. While his citation count of 3 reflects a specialized, early-stage impact, the elegance and utility of his method have made it a valuable reference for researchers tackling scalability in Bayesian nonparametrics. Hanselmann’s approach stands out for its simplicity and effectiveness, offering a clear path for real-time model adaptation. His contributions are particularly relevant for students and practitioners seeking to balance accuracy and computational cost in Gaussian process applications, marking him as a thoughtful contributor to the ongoing evolution of sparse kernel methods.
Research Focus
Key Achievements
Top Papers
- 1