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Bayesian Optimization and Decision Making
Javad Azimi · Roman Garnett · Frank R Hutter · Shakir Mohamed

Fri Dec 07 07:30 AM -- 06:30 PM (PST) @ Emerald Bay 1 +2, Harveys Convention Center Floor (CC)
Event URL: http://javad-azimi.com/nips2012ws/ »

Recent years have brought substantial advances in sequential decision making under uncertainty. These advances have occurred in many different communities, including several subfields of computer science, statistics, and electrical/mechanical/chemical engineering. While these communities are essentially trying to solve the same problem, they develop rather independently, using different terminology: Bayesian optimization, experimental design, bandits, active sensing, personalized recommender systems, automatic algorithm configuration, reinforcement learning, and so on. Some communities focus more on theoretical aspects while others' expertise is on real-world applications. This workshop aims to bring researchers from these communities together to facilitate cross-fertilization by discussing challenges, findings, and sharing data. This workshop follows last year's NIPS workshop "Bayesian optimization, experimental design and bandits: Theory and applications", one of the most-attended workshops in 2011. This year we plan to focus somewhat more on real-world applications, to bridge the gap between theory and practice. Specifically, we plan to have a panel discussion on real-world and industrial applications of Bayesian optimization and an increased focus on real-world applications in the invited talks (covering hyperparameter tuning, configuration of algorithms for solving hard combinatorial problems, energy optimization, and optimization of MCMC). Similar to last year, we expect to highlight the most beneficial research directions and unify the whole community by setting up this workshop.

Author Information

Javad Azimi (Microsoft)
Roman Garnett (Washington University in St. Louis)
Frank R Hutter (Freiburg University)
Shakir Mohamed (DeepMind)
Shakir Mohamed

Shakir Mohamed is a senior staff scientist at DeepMind in London. Shakir's main interests lie at the intersection of approximate Bayesian inference, deep learning and reinforcement learning, and the role that machine learning systems at this intersection have in the development of more intelligent and general-purpose learning systems. Before moving to London, Shakir held a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR), based in Vancouver at the University of British Columbia with Nando de Freitas. Shakir completed his PhD with Zoubin Ghahramani at the University of Cambridge, where he was a Commonwealth Scholar to the United Kingdom. Shakir is from South Africa and completed his previous degrees in Electrical and Information Engineering at the University of the Witwatersrand, Johannesburg.

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