MS-Mo-D-48
Computational learning and model optimization - Part I of II
For Part II, see MS-Mo-E-48

Date: August 10
Time: 13:30--15:30
Room: 212B

(Note: Click title to show the abstract.)

Organizer:
Schönlieb, Carola-Bibiane (Univ. of Cambridge)
Chung, Matthias (Virginia Tech)
De Los Reyes, Juan Carlos (ModeMat)

Abstract: Many scientific fields such as engineering, life sciences, and geophysics encounter large scale problems where observations are contaminated with noise. To infer reliable information from experiments novel modeling techniques and inversion methods are needed. Computational learning and optimized modeling approaches are essential. To target challenges in these fields we will discuss statistical learning methods, optimization and design techniques under uncertainty, and inverse problems of big data.


MS-Mo-D-48-1
13:30--14:00
Designing a realistic image denoising model by means of a bilevel optimization approach.
Calatroni, Luca (Univ. of Cambridge)


MS-Mo-D-48-2
14:00--14:30
How generalised singular vectors can help to develop new regularisation methods
Benning, Martin (Univ. of Cambridge)


MS-Mo-D-48-3
14:30--15:00
Variational nonlinear eigenfunction analysis for signal representation and processing
Gilboa, Guy (Technion)


MS-Mo-D-48-4
15:00--15:30
Derivative-free nonlinear constrained optimization under uncertainty using NOWPAC
Augustin, Florian (Massachusetts Inst. of Tech.)
Marzouk, Youssef (Massachusetts Inst. of Tech.)

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Footnote:
Code: Type-Date-Time-Room No.
Type : IL=Invited Lecture, SL=Special Lectures, MS=Minisymposia, IM=Industrial Minisymposia, CP=Contributed Papers, PP=Posters
Date: Mo=Monday, Tu=Tuesday, We=Wednesday, Th=Thursday, Fr=Friday
Time : A=8:30-9:30, B=10:00-11:00, C=11:10-12:10, BC=10:00-12:10, D=13:30-15:30, E=16:00-18:00, F=19:00-20:00, G=12:10-13:30, H=15:30-16:00
Room No.: TBA