Accession Number:



Games for Computation and Learning

Descriptive Note:

[Technical Report, Final Report]

Corporate Author:

California Institute of Technology

Personal Author(s):

Report Date:


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This project had two main objectives for methods emerging at the interface be-tween game theory, uncertainty quantication, and numerical approximation I their continued application to high impact problems of practical importance in computational mathematics II their development towards machine learning. With this purpose and a dual emphasis on conceptualtheoretical advancements and algorithmiccomputational complexity advancements the accomplishments of this program are as follows. 1 We have developed general robust methods for learning kernels through a hyperparameter tuning via Kernel Flows a variant of cross-validation with applications to learning dynamical systems and to the extrapolation of weather time series, and b programming kernels through interpretable regression networks kernel mode decomposition with applications to empirical mode decomposition.2 We have discovered a very robust and massively parallel algorithm, based on Kullback-Liebler divergence KL minimization that computes accurate approximations of the inverse Cholesky factors of dense kernel matrices with rigorous a priori ON logN log2dN complexity vs. accuracy guarantees

Subject Categories:

  • Computer Programming and Software
  • Operations Research

Distribution Statement:

[A, Approved For Public Release]