M31

Statistical Analysis of Semi-Supervised Regression
John Lafferty and Larry Wasserman Carnegie Mellon University

We study semi-super vised learning using minimax theor y: What assumptions and methods lead to improved minimax rates?

· Rate improvement under manifold assumption · Asymptotic analysis of Laplacian regularization · Manifold-free minimax approaches relating geometries of regression function and data density