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Christopher Genovese

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

NeurIPS Conference 2015 Conference Paper

Optimal Ridge Detection using Coverage Risk

Yen-Chi Chen
Christopher Genovese
Shirley Ho
Larry Wasserman

We introduce the concept of coverage risk as an error measure for density ridge estimation. The coverage risk generalizes the mean integrated square error to set estimation. We propose two risk estimators for the coverage risk and we show that we can select tuning parameters by minimizing the estimated risk. We study the rate of convergence for coverage risk and prove consistency of the risk estimators. We apply our method to three simulated datasets and to cosmology data. In all the examples, the proposed method successfully recover the underlying density structure.

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JMLR Journal 2012 Journal Article

Minimax Manifold Estimation

Christopher Genovese
Marco Perone-Pacifico
Isabella Verdinelli
Larry Wasserman

We find the minimax rate of convergence in Hausdorff distance for estimating a manifold M of dimension d embedded in ℝ D given a noisy sample from the manifold. Under certain conditions, we show that the optimal rate of convergence is n -2/(2+d). Thus, the minimax rate depends only on the dimension of the manifold, not on the dimension of the space in which M is embedded. [abs] [ pdf ][ bib ] &copy JMLR 2012. ( edit, beta )

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NeurIPS Conference 2005 Conference Paper

Active Learning For Identifying Function Threshold Boundaries

Brent Bryan
Robert Nichol
Christopher Genovese
Jeff Schneider
Christopher Miller
Larry Wasserman

We present an efﬁcient algorithm to actively select queries for learning the boundaries separating a function domain into regions where the func- tion is above and below a given threshold. We develop experiment selec- tion methods based on entropy, misclassiﬁcation rate, variance, and their combinations, and show how they perform on a number of data sets. We then show how these algorithms are used to determine simultaneously valid 1 − α conﬁdence intervals for seven cosmological parameters. Ex- perimentation shows that the algorithm reduces the computation neces- sary for the parameter estimation problem by an order of magnitude.

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