no code implementations • NeurIPS 2012 • Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty
We propose an approach to multivariate nonparametric regression that generalizes reduced rank regression for linear models.
no code implementations • NeurIPS 2012 • Han Liu, Larry Wasserman, John D. Lafferty
We prove a new exponential concentration inequality for a plug-in estimator of the Shannon mutual information.
no code implementations • NeurIPS 2010 • Han Liu, Xi Chen, Larry Wasserman, John D. Lafferty
In this paper, we propose a semiparametric method for estimating $G(x)$ that builds a tree on the $X$ space just as in CART (classification and regression trees), but at each leaf of the tree estimates a graph.
no code implementations • NeurIPS 2008 • Han Liu, Larry Wasserman, John D. Lafferty
We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints.
no code implementations • NeurIPS 2007 • Shuheng Zhou, Larry Wasserman, John D. Lafferty
Recent research has studied the role of sparsity in high dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models from sparse data.
no code implementations • 27 Aug 2007 • David M. Blei, John D. Lafferty
This limitation stems from the use of the Dirichlet distribution to model the variability among the topic proportions.
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