NTR Webinar: Algorithm Selection: How to Get the Most Out of Your Algorithm Portfolio

NTR organizes and hosts scientific webinars on neural networks and invites speakers from all over the world to present their recent work at the webinars.

On June 8 Damir Pulatov, University of Wyoming, Laramie, WY, USA, led a technical Zoom webinar on How to Get the Most Out of Your Algorithm Portfolio

About the webinar: 

As often happens in practice, algorithms end up having complementary performance. Therefore, choosing the right solver or algorithm for a given problem can have a major impact on how much time it takes to solve that particular problem. 

Algorithm selection is a field of study that automates the process of selecting algorithms for a given instance of a problem. It is traditionally done by treating algorithms as black boxes where nothing outside of algorithms’ past performance and problem features is known during the selection process.

In our work we are trying to automatically characterize algorithms to improve the algorithm selection process which in turn means solving problems faster and more efficiently.

Bischl, B. and Kerschke, P. and Kotthoff, L. and Lindauer, M. and Malitsky, Y. and Frechétte, A. and Hoos, H. and Hutter, F. and Leyton-Brown, K. and Tierney, K. and Vanschoren, J.

ASlib: A Benchmark Library for Algorithm Selection

In: Artificial Intelligence Journal (AIJ) (2016), pages 41–58

Pulatov, D., & Kotthoff, L. (2020). Opening the Black Box: Automatically Characterizing Software for Algorithm Selection (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence34(10), 13899-13900. https://doi.org/10.1609/aaai.v34i10.7222

Materials available:

Webinar presentation

Moderator and contact: 

NTR CEO Nick Mikhailovsky: nickm@ntrlab.com.

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