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Larry Wasserman. This paper is about two related methods for two sample testing and independence testing which have emerged over the last decade: Maximum Mean Discrepancy (MMD) for the former ...
Dec 11, 2013 · Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics.
36-708 Statistical Methods for Machine Learning. Instructor: Larry Wasserman Lecture Time: Tuesday and Thursday 1:30 - 2:50 Lecture Location: POS 152. Office Hour: Tuesdays 12:00 - 1:00 Baker Hall 132G Office: Baker Hall 132G Email: larry@stat.cmu.edu.
Larry Wasserman is a statistician known for his work on high dimensional inference, nonparametric inference, machine learning, topological data analysis and astrostatistics. Much of his work has been devoted to providing statistical foundations for algorithms in machine learning. Wasserman was born in Windsor, Ontario.
Dec 4, 2003 · Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference,asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics.
- Larry Wasserman
36-401 Modern Regression. Instructor: Larry Wasserman Time: Tuesday and Thursday 12:00 - 1:20 Place: PH 100 Office Hour: Tuesdays 1:30 - 2:30 Baker Hall 132G TA Information TA: Collin Eubanks (Head TA)
Dec 1, 2010 · Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference,asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics.
- Larry Wasserman
