VADOR Events Calendar

Our team is constantly involved in research projects, frequently involving collaboration with international scientists and institutions. Research is carried out in a number of languages, however we present mostly in English.

We frequently host one off lectures on topics relating to variational analysis, dynamics and operations research.  In term-time, we host different speakers at our weekly AKOR seminar.  Seminars take place most Thursdays at 3pm in Sem. R. DB gelb 04.  Once a month, the AKOR seminar will be replaced by the Vienna Seminar on Optimization, opens an external URL in a new window - a joint venture with Radu Bot and Yurii Malitskyi of the University of Vienna

We organise the Viennese Conference on Optimal Control and Dynamic Games, typically every three years.  The next iteration - VC2025 - will take place in July 2025.  For further details on this conference, and its forerunners, please visit the VC2025, opens an external URL in a new window website.

Topics and speakers for all forthcoming events will be posted below.

13. June 2024, 15:00 until 17:00

AKOR Seminar A convex Formulation for Total-Variation Parameter Learning

Seminar

Enis Chenchene, University of Vienna

We present a new approach for data-driven tuning of regularization parameters for total-variation denoising.
The proposed approach can be understood in connection to a weak optimal transport problem between the distributions of clean and noisy data and admits a manageable convex formulation that can be solved efficiently with a new conditional-gradient-type method. We show numerical experiments and open avenues for promising extensions.

Calendar entry

Event location

Sem R DB 04 gelb
1040 Wien

 

Organiser

VADOR
vador@tuwien.ac.at

 

Public

No

 

Entrance fee

No

 

Registration required

No

AKOR Seminar A convex Formulation for Total-Variation Parameter Learning

Enis Chenchene, University of Vienna

We present a new approach for data-driven tuning of regularization parameters for total-variation denoising.
The proposed approach can be understood in connection to a weak optimal transport problem between the distributions of clean and noisy data and admits a manageable convex formulation that can be solved efficiently with a new conditional-gradient-type method. We show numerical experiments and open avenues for promising extensions.