Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter June 11, 2013

Empirical Inference

  • Bernhard Schölkopf


Empirical Inference is the process of drawing conclusions from observational data. For instance, the data can be measurements from an experiment, which are used by a researcher to infer a scientific law. Another kind of empirical inference is performed by living beings, continuously recording data from their environment and carrying out appropriate actions. Do these problems have anything in common, and are there underlying principles governing the extraction of regularities from data? What characterizes hard inference problems, and how can we solve them? Such questions are studied by a community of scientists from various fields, engaged in machine learning research.

This short paper, which is based on the authorüs lecture to the scientific council of the Max Planck Society in February 2010, will attempt to describe some of the main ideas and problems of machine learning. It will provide illustrative examples of real world machine learning applications, including the use of machine learning towards the design of intelligent systems.

* Correspondence address Prof. Bernhard Schölkopf Max Planck Institute for Intelligent Systems Spemannstr. 38, Tübingen, Germany Tel.: +49 7071 601 551 Fax: +49 7071 601 552 E-mail:


[1] G.W.Leibniz: Discours de métaphysique. (cited after Chaitin, 2010), 1686.Search in Google Scholar

[2] H.B.Barlow, in: M.Gazzaniga (Ed.), The neuron doctrine in perception. The Cognitive Neurosciences, MIT Press, Cambridge, MA, 1995.Search in Google Scholar

[3] H.Helmholtz: Handbuch der physiologischen Optik, Leopold Voss, Leipzig, 1867.Search in Google Scholar

[4] S.Sonnenburg, G.Rätsch, C.Schäfer, B.Schölkopf: Journal of Machine Learning Research7 (2006) 15311565.Search in Google Scholar

[5] B.Schölkopf, A.J.Smola: Learning with Kernels, MIT Press, Cambridge, MA, 2002.Search in Google Scholar

[6] M.Hutter: Universal artificial intelligence, Springer, Berlin, 2005.10.1007/b138233Search in Google Scholar

[7] J.Watkins: Karl Raimund Popper 1902–1994. Proceedings of the British Academy 96 (1997) 645684.Search in Google Scholar

[8] V.N.Vapnik: Statistical Learning Theory, Wiley, New York, 1998.Search in Google Scholar

[9] C.Walder, B.Schölkopf, O.Chapelle: Comput. Graphics Forum25 (2006) 635644. (Eurographics).10.1111/j.1467-8659.2006.00983.xSearch in Google Scholar

[10] B.Schölkopf, F.Steinke, V.Blanz, in: L. De Raedt, S. Wrobel Eds., Object correspondence as a machine learning problem. Proceedings of the 22nd International Conference on Machine Learning, pages 777–784, New York, 2005. ACM Press.10.1145/1102351.1102449Search in Google Scholar

[11] M.Hofmann, F.Steinke, V.Scheel, G.Charpiat, J.Farquhar, P.Ascho, M.Brady, B.Schölkopf, B.J.Pichler: J. Nucl. Med.49 (2008) 18751883.10.2967/jnumed.107.049353Search in Google Scholar PubMed

[12] J.Kober, K.Mülling, O.Krömer, C.H.Lampert, B.Schölkopf, J.Peters: Movement templates for learning of hitting and batting, in: Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), pages 853–858, Piscataway, NJ, U.S.A., 2010. IEEE.10.1109/ROBOT.2010.5509672Search in Google Scholar

[13] P.Daniušis, D.Janzing, J.Mooij, J.Zscheischler, B.Steudel, K.Zhang, B.Schölkopf: Inferring deterministic causal relations, in: 26th Conference on Uncertainty in Artificial Intelligence, Corvallis, OR, U.S.A., 07 2010. AUAI Press.Search in Google Scholar

Received: 2011-3-18
Accepted: 2011-5-4
Published Online: 2013-06-11
Published in Print: 2011-07-01

© 2011, Carl Hanser Verlag, München

Downloaded on 10.6.2023 from
Scroll to top button