Optimization for Machine Learning
This workshop builds on precedent established the previous OPT workshops:
- (@NIPS*08): http://opt2008.kyb.tuebingen.mpg.de/
- (@NIPS*09): http://opt.kyb.tuebingen.mpg.de/opt09/
- (@NIPS*10): http://opt.kyb.tuebingen.mpg.de/opt10/
Both these workshops had packed (often overpacked) attendance almost throughout the day. This enthusiastic reception reflects the strong interest, relevance, and importance enjoyed by optimization in the greater ML community.
One could ask why does optimization attract such continued interest? The answer is simple but telling: optimization lies at the heart of almost every ML algorithm. For some algorithms textbook methods suffice, but the majority requires tailoring algorithmic tools from optimization; moreover, this tailoring depends on a deeper understanding of the ML requirements. In fact, ML applications and researchers are driving some of the most cutting-edge developments in optimization today. The intimate relation of optimization with ML is the key motivation for our workshop, which aims to foster discussion, discovery, and dissemination of the state-of-the-art in optimization, especially in the context of ML.
Workshop homepage: http://opt.kyb.tuebingen.mpg.de/index.html
Event sectionsHome Cosmology meets Machine Learning From Statistical Genetics to Predictive Models in Personalized Medicine New Frontiers in Model Order Selection
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