Open Collections

Interactive Bayesian optimization : learning user preferences for graphics and animation

University of British Columbia

Bayesian optimization with Gaussian processes has become increasingly popular in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it useful for optimizing expensive black box functions. We examine the case of using Bayesian optimization when the objective function requires feedback from a human. We call this class of problems \emph{interactive} Bayesian optimization. Here, we assume a parameterized model, and a user whose task is to find an acceptable set of parameters according to some perceptual value function that cannot easily be articulated. This requires special attention to the qualities that make this a unique problem, and so, we introduce three novel extensions: the application of Bayesian optimization to "preference galleries", where human feedback is in the form of preferences over a set of instances; a particle-filter method for learning the distribution of model hyperparameters over heterogeneous users and tasks; and a bandit-based method of using a portfolio of utility functions to select sample points. Using a variety of test functions, we validate our extensions empirically on both low- and high-dimensional objective functions. We also present graphics and animation applications that use interactive Bayesian optimization techniques to help artists find parameters on difficult problems. We show that even with minimal domain knowledge, an interface using interactive Bayesian optimization is much more efficient and effective than traditional "parameter twiddling" techniques on the same problem.

Text

2011-01-04

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/30519

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/