270 citations · 352 across the 19 of their papers we have counts for
4 papers · 1 filter
Statistical Analysis of Wasserstein Distributionally Robust Estimators
Jose Blanchet, Karthyek Murthy, Viet Anh Nguyen
We consider statistical methods which invoke a min-max distributionally robust formulation to extract good out-of-sample performance in data-driven optimization and learning proble…
Efficient Black-Box Importance Sampling for VaR and CVaR Estimation
Anand Deo, Karthyek Murthy
This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined in terms of a sophisticated object such as a machine learning feature map or a mixe…
Testing Group Fairness via Optimal Transport Projections
Nian Si, Karthyek Murthy, Jose Blanchet +1
We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions. The proposed test is a flexible…
Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers
Anand Deo, Karthyek Murthy
This paper presents a novel Importance Sampling (IS) scheme for estimating distribution tails of performance measures modeled with a rich set of tools such as linear programs, inte…