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Bayesian Active Learning for Sensitivity Analysis


Conference Paper


Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore the behavior of the system, and to compute sensitivity measures as expectations over the input distribution. Monte Carlo methods are a simple approach to estimate these integrals, but they are infeasible when the models are computationally expensive. Using a Gaussian processes prior, expensive simulation runs can be saved. This Bayesian quadrature allows for an active selection of inputs where the simulation promises to be most valuable, and the number of simulation runs can be reduced further. We present an active learning scheme for sensitivity analysis which is rigorously derived from the corresponding Bayesian expected loss. On three fully featured, high dimensional physical models of electro-mechanical sensors, we show that the learning rate in the active learning scheme is significantly better than for passive learning.

Author(s): Pfingsten, T.
Book Title: ECML 2006
Journal: Machine Learning: ECML 2006
Pages: 353-364
Year: 2006
Month: September
Day: 0
Editors: F{\"u}rnkranz, J. , T. Scheffer, M. Spiliopoulou
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/11871842_35
Event Name: 17th European Conference on Machine Learning
Event Place: Berlin, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Bayesian Active Learning for Sensitivity Analysis},
  author = {Pfingsten, T.},
  journal = {Machine Learning: ECML 2006},
  booktitle = {ECML 2006},
  pages = {353-364},
  editors = {F{\"u}rnkranz, J. , T. Scheffer, M. Spiliopoulou},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2006},
  month_numeric = {9}