2 citations · 3 across the 3 of their papers we have counts for
5 papers
Mixture Proportion Estimation via Kernel Embedding of Distributions
Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari
Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component. This problem con…
A Mutual Contamination Analysis of Mixed Membership and Partial Label Models
Julian Katz-Samuels, Clayton Scott
Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a s…
Sparse Approximation of a Kernel Mean
E. Cruz Cortés, C. Scott
Kernel means are frequently used to represent probability distributions in machine learning problems. In particular, the well known kernel density estimator and the kernel mean emb…
Adaptive Hausdorff estimation of density level sets
Aarti Singh, Clayton Scott, Robert Nowak
Consider the problem of estimating the -level set of an unknown -dimensional density function based on independent observations …
The False Discovery Rate for Statistical Pattern Recognition
Clayton Scott, Gowtham Bellala, Rebecca Willett
The false discovery rate (FDR) and false nondiscovery rate (FNDR) have received considerable attention in the literature on multiple testing. These performance measures are also ap…