2 citations · 5 across the 12 of their papers we have counts for
16 papers
Learning from data with structured missingness
Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti +17
Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. H…
Covering of high-dimensional sets
Anatoly Zhigljavsky, Jack Noonan
Let be a metric space and be a Borel measure on this space defined on the -algebra generated by open subsets of ; this measure defines vol…
Improving exploration strategies in large dimensions and rate of convergence of global random search algorithms
Jack Noonan, Anatoly Zhigljavsky
We consider global optimization problems, where the feasible region $\X$ is a compact subset of with . For these problems, we demonstrate the following. F…
An integrated approach to test for missing not at random
Jack Noonan, Adetola Adedamola Adediran, Robin Mitra +1
Missing data can lead to inefficiencies and biases in analyses, in particular when data are missing not at random (MNAR). It is thus vital to understand and correctly identify the…
Online change-point detection for a transient change
Jack Noonan
We consider a popular online change-point problem of detecting a transient change in distributions of i.i.d. random variables. For this change-point problem, several change-point p…
Random and quasi-random designs in group testing
Jack Noonan, Anatoly Zhigljavsky
For large classes of group testing problems, we derive lower bounds for the probability that all significant items are uniquely identified using specially constructed random design…