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Optimal Regularization for a Data Source
In this paper, we seek a systematic understanding of the power and the limitations of convex regularization by investigating the following questions: Given a distribution, what is the optimal regularizer for data drawn from the distribution?
Spectrahedral Regression
Convex regression is the problem of fitting a convex function to a data set consisting of input-output pairs.
Minimax Rates for High-Dimensional Random Tessellation Forests
In this work, we show that a large class of random forests with general split directions also achieve minimax optimal convergence rates in arbitrary dimension.
Stochastic geometry to generalize the Mondrian Process
This allows for precise comparisons between the Mondrian forest, the Mondrian kernel and the Laplace kernel in density estimation.
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