Statistical Learning Seminars
The effects of CoViD-19 pervade through research communities across the globe, causing canceled conferences, postponed research visits, and suspended projects. Like many others, we have sought other opportunities for collaboration in spite of the current state of affairs and have therefore organized this online seminar series in statistical learning.
We use zoom. Each seminar is 1 hour long, with roughly 50 minutes allocated to the presentation(s) and 10 minutes to discussions. You will be placed in a waiting room upon entering the seminar. Please wait for the host to let you in to the meeting.
The seminar is held on a monthly basis on Fridays, starting at 15:30 CEST. See Previous Talks for recordings, slides, and resources from previous seminars.
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Link to calendar event
January 29, 15:30 CEST
Michael Weylandt (University of Florida)
- Convex Clustering: Methods, Theory, Algorithms, and Visualizations
- Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. The past few years have seen a surge of interest in convex clustering and breakthroughs in associated theory and methodology. In this talk, I will review this exciting literature, with a particular focus on novel computational strategies developed to construct dendrograms and interactive visualizations from convex clustering approaches. This talk reflects joint work with Genevera Allen, Yue Hu, George Michailidis, John Nagorski, Mitch Rodenberry, Minjie Wang, and Tianyi Yao.
- Related Papers
This seminar series is a joint effort organized by The Department of Mathematics, Wrocław University, The Department of Mathematics, University of Burgundy, and The Department of Statistics, Lund University.