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 for all the sessions. Upon joining the seminar, you will be placed in a waiting room; please wait for the host to let you in to the meeting.
The seminars are approximately an hour long with anywhere between 20 and 40 minutes allocated to the presentation and the rest for discussion. Sessions are held on a regular basis on Fridays at 15:30 CET. See Previous Talks for recordings, slides, and resources from previous seminars.
To receive announcements for upcoming seminars, please join the group at https://groups.google.com/g/statlearnsem.
Link to calendar event
Friday, March 4 15:30 CET
Nghia Tran (Oakland University)
- Sharp, strong, and unique minimizers for low complexity robust recovery
- In this talk, we show the important roles of sharp minima and strong minima for robust recovery. We also obtain several characterizations of sharp minima for convex regularized optimization problems. Our characterizations are quantitative and verifiable especially for the case of decomposable norm regularized problems including sparsity, group-sparsity, and low-rank convex problems. For group-sparsity optimization problems, we show that a unique solution is a strong solution and obtain quantitative characterizations for solution uniqueness.
- Related Work
- Sharp, strong and unique minimizers for low complexity robust recovery
- Nghia Tran is an associate professor at the Department of Mathematics and Statistics at Oakland University, Rochester, Michigan, USA. His primary research focus has been non-smooth optimization, variational analysis, and applications thereof.
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.