76 citations · 156 across the 7 of their papers we have counts for
8 papers
Variational Information Pursuit for Interpretable Predictions
Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele +2
There is a growing interest in the machine learning community in developing predictive algorithms that are "interpretable by design". Towards this end, recent work proposes to make…
Interpretable by Design: Learning Predictors by Composing Interpretable Queries
Aditya Chattopadhyay, Stewart Slocum, Benjamin D. Haeffele +2
There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms. Providing an explanation of the reasoning process in domain-spe…
Information Pursuit: A Bayesian Framework for Sequential Scene Parsing
Ehsan Jahangiri, Erdem Yoruk, Rene Vidal +2
Despite enormous progress in object detection and classification, the problem of incorporating expected contextual relationships among object instances into modern recognition syst…
Coarse-to-fine Multiple Testing Strategies
Kamel Lahouel, Donald Geman, Laurent Younes
We analyze control of the familywise error rate (FWER) in a multiple testing scenario with a great many null hypotheses about the distribution of a high-dimensional random variable…
Tail Risk Constraints and Maximum Entropy
Donald Geman, Hélyette Geman, Nassim Nicholas Taleb
In the world of modern financial theory, portfolio construction has traditionally operated under at least one of two central assumptions: the constraints are derived from a utility…
Rank discriminants for predicting phenotypes from RNA expression
Bahman Afsari, Ulisses M. Braga-Neto, Donald Geman
Statistical methods for analyzing large-scale biomolecular data are commonplace in computational biology. A notable example is phenotype prediction from gene expression data, for i…