activity
20162020
most citedScalable Information Inequalities for Uncertainty Quantification

25 citations · 42 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2020★ 3 cited

Variational Representations and Neural Network Estimation of Rényi Divergences

Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis +2

We derive a new variational formula for the Rényi family of divergences, , between probability measures and . Our result generalizes the classical Donsker-Varadha…

math.PR2019★ 3 cited

Distributional Robustness and Uncertainty Quantification for Rare Events

Jeremiah Birrell, Paul Dupuis, Markos A. Katsoulakis +2

Rare events, and more general risk-sensitive quantities-of-interest (QoIs), are significantly impacted by uncertainty in the tail behavior of a distribution. Uncertainty in the tai…

cs.IR2017★ 1 cited

DTATG: An Automatic Title Generator based on Dependency Trees

Liqun Shao, Jie Wang

We study automatic title generation for a given block of text and present a method called DTATG to generate titles. DTATG first extracts a small number of central sentences that co…

cs.IT2017★ 10 cited

How biased is your model? Concentration Inequalities, Information and Model Bias

Konstantinos Gourgoulias, Markos A. Katsoulakis, Luc Rey-Bellet +1

We derive tight and computable bounds on the bias of statistical estimators, or more generally of quantities of interest, when evaluated on a baseline model P rather than on the ty…

cs.IT2016★ 25 cited

Scalable Information Inequalities for Uncertainty Quantification

Markos A. Katsoulakis, Luc Rey-Bellet, Jie Wang

In this paper we demonstrate the only available scalable information bounds for quantities of interest of high dimensional probabilistic models. Scalability of inequalities allows…