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SUMMARY:Bayesian Optimization for Accelerated Exploration of Chemical Spac
 e - José Miguel Hernández Lobato\, University of Cambridge
DTSTART:20161117T150000Z
DTEND:20161117T160000Z
UID:TALK69165@talks.cam.ac.uk
CONTACT:Tim Hughes
DESCRIPTION:Chemical space is so large as to make a brute force search for
  molecules with improved properties infeasible. Bayesian optimization meth
 ods can accelerate the discovery process by sequentially identifying the m
 ost useful experiments to be performed next. However\, existing methods ha
 ve shortcomings that limit their applicability to the molecule search prob
 lem. First\, they lack scalability to the large amounts of data that are r
 equired to successfully navigate chemical space. Second\, they are unable 
 to learn feature representations for the data\, which reduces their statis
 tical efficiency in the large data scenario. Third\, they cannot collect d
 ata using very large batch sizes\, which is required when many experiments
  can be performed simultaneously and finally\, they often fail when the se
 arch space is discrete as is the case of chemical space. In this talk I wi
 ll give a brief introduction to Bayesian optimization methods and then I w
 ill present different contributions that aim to solve or at least alleviat
 e the aforementioned problems.
LOCATION:Cambridge University Engineering Department\, LR4
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