Documentation

This Python module generates designs of experiments based on kernel methods such as kernel herding and support points. Additionally, the TestSetWeighting provides a new estimator for validation designs.

Theory

• Principle of kernel-based sampling methods

User documentation

• Index of classes
  • KernelHerding
  • KernelHerdingTensorized
  • GreedySupportPoints
  • TestSetWeighting
  • BayesianQuadratureWeighting

Examples

• Examples index
  • Kernel herding examples
  • Energy computation example
  • Machine learning validation example
  • Bayesian quadrature weighting example

References

• Chen, Y., M. Welling, & A. Smola (2010). Super-samples from kernel herding. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, pp. 109 – 116.

• Mak, S. & V. R. Joseph (2018). Support points. The Annals of Statistics 46, 2562 – 2592.

• Fekhari, E., B. Iooss, J. Mure, L. Pronzato, & M. Rendas (2022). Model predictivity assessment: incremental test-set selection and accuracy evaluation. preprint.

• Briol, F.-X., C. Oates, M. Girolami, M. Osborne, & D. Sejdinovic (2019). Probabilistic Integration: A Role in Statistical Computation? Statistical Science 34, 1 – 22.

• Huszár, F. & D. Duvenaud (2012). Optimally-Weighted Herding is Bayesian Quadrature. In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, pp. 377 – 386.

• Teymur, O., J. Gorham, M. Riabiz, & C. Oates (2021). Optimal quantisation of probability measures using maximum mean discrepancy. In International Conference on Artificial Intelligence and Statistics, pp. 1027 – 1035.