Machine learning in the service of understanding human learning: an ideal observer-based analysis of the learning curve
Ferenc Huszár, Uta Noppeney, Máté Lengyel · MPG.PuRe (Max Planck Society) · 2008
Preliminary Schedule.There has recently been a surge of interest in algebraic methods in machine learning.This includes new approaches to ranking problems, the budding field of algebraic statistics and various applications of non-commutative Fourier transforms.The aim of the workshop is to bring together these distinct communities, explore connections, and showcase algebraic methods to the machine learning community at large.The symposium is intended to be accessible to researchers with no prior exposure to abstract algebra.The program includes three short tutorials that will cover the basic concepts necessary for understanding cutting edge research in the field.13.30-14.00Non-commutative harmonic analysis Risi Kondor 14.05-14.35Modeling distributions on permutations and partial ranking Guy Lebanon 14.45-15.05Algebraic models for multilinear dependence Jason Morton 15.10-15.40Symmetry Group-based Learning for Regularity Discovery from Real World Patterns Yanxi Liu 15.45-16.15Estimation and model selection in stagewise ranking -a representation story Marina MeilaNon-commutative harmonic analysis Risi Kondor, Gatsby Unit, UCL Fourier analysis is one of the central pillars of applied mathematics.Representation theory makes it possible to generalize Fourier transformation to non-commutative groups, such as permutations and 3D rotations.This talk will survey new applications of this theory in machine learning for problems such as identity management in multi-object tracking, transformation invariant representations of images, and similarity measures between graphs.The talk is intended for a wide audience, no background in group theory or representation theory will be assumed. Modeling distributions on permutations and partial ranking Guy Lebanon, Georgia Institute of Technology