Professor Dr Manfred K. Warmuth
- Section Informatics
- Location Santa Cruz, CA, United States
- Election year 2021
Research
Research Priorities: Computational learning theory, online algorithms, machine learning, discrete mathematics, artificial intelligence
Manfred K. Warmuth is a German computer scientist who conducts research into computational learning theories with a special focus on online learning algorithms,
including weighted majority and exponentiated gradient algorithms. A pioneer of online algorithm deduction and analysis, Manfred Warmuth developed a number of methods aimed at making algorithms robust to changing data. His most recent research has been dedicated to the development of matrix parameter online algorithms. He was able to generalize the multiplicative updates of probability vectors to density matrices and developed a Bayesian probability calculation for density matrices.
Another one of Manfred K. Warmuth’s research priorities is machine learning – a field in which he described several problems previously unsolved by the scientific community. This includes a presumed compression scheme according to which every Vapnik-Chervonenkis dimension d concept class sample can be compressed to subsets of size equal to d.
Manfred K. Warmuth is a German computer scientist who conducts research into computational learning theories with a special focus on online learning algorithms,
including weighted majority and exponentiated gradient algorithms. A pioneer of online algorithm deduction and analysis, Manfred Warmuth developed a number of methods aimed at making algorithms robust to changing data. His most recent research has been dedicated to the development of matrix parameter online algorithms. He was able to generalize the multiplicative updates of probability vectors to density matrices and developed a Bayesian probability calculation for density matrices.
Another one of Manfred K. Warmuth’s research priorities is machine learning – a field in which he described several problems previously unsolved by the scientific community. This includes a presumed compression scheme according to which every Vapnik-Chervonenkis dimension d concept class sample can be compressed to subsets of size equal to d.
Career
- 2019-2020 Visiting Faculty Member, Google Brain, Mountain View, USA
- 2019 Visiting Faculty Member, Google Brain, Zurich, Switzerland
- 2018 Emeritus, Distinguished Professor of Computer Science, University of California, Santa Cruz, USA
- 2017 Distinguished Professor (Off Scale) of Computer Science, University of California, Santa Cruz, USA
- 2013 Professor (Step 9) of Computer Science, University of California, Santa Cruz, USA
- 2010 Professor (Step 8) of Computer Science, University of California, Santa Cruz, USA
- 2007 Faculty Visitor, Google Brain, Mountain View, USA
- 2007 Professor (Step 7) of Computer Science, University of California, Santa Cruz, USA
- 2004 Professor (Step 6) of Computer Science, University of California, Santa Cruz, USA
- 2004 Senior Principal Researcher, National ICT Australia Ltd (NICTA), Canberra, Australia
- 1993 Scientific Advisor, AT&T Bell Labs, Murray Hill, USA
- 1992-2004 Professor of Computer Science, University of California, Santa Cruz, USA
- 1990 Visiting Research Fellow, Fujitsu International Institute for Advanced Study of Social Information Science, Numazu, Japan
- 1988-1992 Associate Professor of Computer and Information Sciences, University of California, Santa Cruz, USA
- 1988-1989 Visiting Fellow, Computer Science, Harvard University, Cambridge, USA
- 1983-1988 Assistant Professor of Computer and Information Sciences, University of California, Santa Cruz, USA
- 1984-1985 Postdoc, The Hebrew University of Jerusalem, Jerusalem, Israel
- 1982-1983 Visiting Lecturer, University of California, Berkeley, USA
- 1982 Visiting Lecturer, University of California, Santa Cruz, USA
- 1981 PhD in Computer Science, University of Colorado, Boulder, USA
- 1980 Master of Science in Computer Science, University of Colorado, Boulder, USA
Functions
- since 2001 Action Editor, Journal of Machine Learning Research
- 1999-2001 Coeditor, Machine Learning
- 1993-1996 Coeditor, Information and Computation
Projects
- 2016-2019 “On-Line Learning Algorithms for Path Experts with Non-Additive Losses” Project, Division of Information and Intelligent Systems, Robust Intelligence, National Science Foundation (NSF), USA
- 2011-2016 Project Manager “Probabilistic Models using Generalized Exponential Families”, Division of Information and Intelligent Systems, Robust Intelligence, NSF, USA
- 2010 Project Manager “Combining Ranked Lists”, Google Gift Grant, Google Foundation, Mountain View, USA
- 2009-2012 „Kernelization with Outer Product Instances” Project, Division of Information and Intelligent Systems, Robust Intelligence, NSF, USA
- 2003-2008 “Representation and Learning in Computational Game Theory” Project, Medium ITR, Division of Information and Intelligent Systems, NSF, USA
- 2003-2005 “Analysing Iterative Machine Learning Algorithms with Information Geometry” Project, Australian Research Council, Australia
- 1999-2002 “Deriving and Analyzing Learning Algorithms” Project, NSF Grant, NSF, USA
- 1997-1999 “Amortized Analysis for On-Line Learning Algorithms” Project, NSF Grant, NSF, USA
- 1992-1994 “Machine Learning” Project, NSF Grant, NSF, USA
- 1986-1992 “Machine Learning” Project, Office of Naval Research, Arlington, USA
Honours and Memberships
- since 2021 Member, German National Academy of Sciences Leopoldina, Germany
- 1984-1985 Scholarship, Fellow, US-Israel Binational Science Foundation, Jerusalem, Israel