Prof. Dr. rer. nat.

Ingo Steinwart

Principal Investigator (EXC), Koordinator PN 6
Institut für Stochastik und Anwendungen

Kontakt

+49 711 685-65388

Website

Pfaffenwaldring 57
D-70569 Stuttgart
Deutschland
Raum: 8.544

Fachgebiet

  • Statistische Lerntheorie
  • Kernbasierte Lernverfahren (Support Vector Machines)
  • Reproduzierende Kern-Hilberträume
  • Entropie- und Überdeckungszahlen
  • Cluster Analysis
  • Verlustfunktionen für spezielle Probleme wie Quantilregression und Ausreißeridentifikation
  • Effiziente Implementierungen von Lernverfahren
  • Anwendungen von Lernverfahren

Ausgewählte Publikationen

  1. H. Hang and I. Steinwart. “A Bernstein-type inequality for some mixing processes and dynamical systems with an  application to learning”. In: The Annals of Statistics 45.2 (2017), pp. 708–743.
  2. I. Steinwart. “Fully adaptive density-based clustering”. In: The Annals of Statistics 43.5 (2015), pp. 2132–2167.
  3. I. Steinwart, D. Hush, and C. Scovel. “Training SVMs without offset”. In: Journal of Machine Learning Research 12 (2011), pp. 141–202.
  4. I. Steinwart and M. Anghel. “An SVM approach for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise.” In: Annals of Statistics 37.2 (2009), pp. 841–875.
  5. I. Steinwart, D. Hush, and C. Scovel. “Learning from dependent observations”. In: Journal of Multivariate Analysis 100.1 (2009), pp. 175–194.
  6. I. Steinwart and A. Christmann. Support vector machines. Springer, 2008.
  7. I. Steinwart. “How to compare different loss functions and their risks”. In: Constructive Approximation 26.2 (2007), pp. 225–287.
  8. I. Steinwart and C. Scovel. “Fast rates for support vector machines using Gaussian kernels”. In: The Annals of Statistics 35.2 (2007), pp. 575–607.
  9. I. Steinwart, D. Hush, and C. Scovel. “A classification framework for anomaly detection”. In: Journal of Machine Learning Research 6 (2005), pp. 211–232.
  10. I. Steinwart. “On the influence of the kernel on the consistency of support vector machines”. In: Journal of Machine Learning Research 2 (2001), pp. 67–93.

Arbeitserfahrung

  • seit 2010 Director of the Institute for Stochastics and Applications, University of Stuttgart
  • seit 2010 Full Professor for Stochastics, University of Stuttgart
  • seit 2010 Member of the Senate Committee for Organisation, University Stuttgart
  • 2011–2012 Vice-Dean of the Faculty for Mathematics and Physics, University of Stuttgart
  • 2010–2011 Associate Adjunct Professor, Department of Computer Science, University of California, Santa Cruz, USA
  • 2008–2010 Scientist Level 4, Los Alamos National Laboratory, USA
  • 2003–2008 Technical Staff Member (permanent position since 2005), Los Alamos National Laboratory, USA
  • 2002 Visiting Researcher, Gutenberg-University, Mainz
  • 2000–2003 Scientific Staff Member, Friedrich-Schiller-University, Jena
  • 1997–2000 Stipendiary, DFG graduate college “Analytic and Stochastic Structures and Systems”, Friedrich-Schiller-University, Jena

 

Ausbildung und Akademische Grade

  • 2000 Dr.rer.nat., Mathematics, Friedrich-Schiller-University, Jena
  • 1997 Diploma in Mathematics, Carl-von-Ossietzky University, Oldenburg
  • 1991–1997 Study of Mathematics, Carl-von-Ossietzky University, Oldenburg
  • seit 2017 Faculty Member, Internat. Max Planck Research School for Intelligent Systems
  • 2014 Semi-plenary speaker, Foundations of Computational Mathematics, Montevideo
  • 2013 Visiting Researcher, National ICT Australia, Canberra
  • 2010 Semi-plenary speaker, 42èmes Journées de Statistique de la SFdS, Marseille
  • 2009 Principal Researcher (team leader position), National ICT Australia, declined
  • 2004 Los Alamos National Laboratory Achievement Award, 2004
  • 2001 Ph.D. prize for the best Ph.D. thesis in Mathematics, Jena
  • Associate Editor “Annals of Statistics” (2010–2012)
  • Action Editor (Associate Editor) “Journal of Machine Learning Research” (since 2008)
  • Associate Editor “Journal of Complexity” (since 2013)
  • Program Chair, Conference on Learning Theory (COLT) (2013)
  • Area Chair, Neural Information Processing Systems (2008, 2011)
  • Program Committee, Conference on Learning Theory (2006, 2008, 2009, 2011, 2012, 2015)
  • Mini-course, Machine Learning Summer School, Tübingen (2013)
  • Workshop co-Organizer, Oberwolfach Research Institute for Mathematics (2011)
  • Session Organizer, German Probability and Statistics Days, Bochum (2016)
  • Reviewer for e.g. DFG, NSF, Austrian Academy of Science, Research Grants Council Hong Kong, National Science Centre Poland, Mercator Foundation, Einstein Foundation
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