Using Machine Learning to Improve Monte Carlo and Blackbox Optimization

  • David Wolpert (Santa Fe Institute and Los Alamos National Laboratory, USA)
A3 01 (Sophus-Lie room)


In this talk I explore the relationship between Machine-Learning (ML), Monte Carlo Integral Estimation (MC), and 'blackbox' optimization (BO - the kind of optimization one typically addresses with genetic algorithms or simulated annealing).

To begin, I show how to to apply ML to automatically set annealing temperatures and other hyperparameters of *any* stochastic BO algorithm. Then I show how to extend this, first to transform any BO problem into a particular MC problem, and then to show that this MC problem is formally identical to the problem of how to do supervised learning. This extension allows us to apply *all* of the powerful techniques that have created for supervised learning to solve BO problems. I demonstrate the power of this in experiments (movies!).

I end by showing how to improve the convergence of any of a broad set of MC algorithms, by using the ML technique of stacking to learn control variates.

11.02.02 22.04.20

Complex Systems Seminar

MPI for Mathematics in the Sciences Live Stream

Katharina Matschke

MPI for Mathematics in the Sciences Contact via Mail