Search Results for author:
Found 10 papers, 1 papers with code
Importance Sampling for Minimization of Tail Risks: A Tutorial
This paper provides an introductory overview of how one may employ importance sampling effectively as a tool for solving stochastic optimization formulations incorporating tail risk measures such as Conditional Value-at-Risk.
A Nonparametric Approach with Marginals for Modeling Consumer Choice
The marginal distribution model (MDM) is one such model, that requires only the specification of marginal distributions of the random utilities.
Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk
This paper investigates the use of retrospective approximation solution paradigm in solving risk-averse optimization problems effectively via importance sampling (IS).
Statistical Analysis of Wasserstein Distributionally Robust Estimators
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 problems.
Efficient Black-Box Importance Sampling for VaR and CVaR Estimation
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 mixed integer linear optimisation formulation.
Testing Group Fairness via Optimal Transport Projections
We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions.
Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers
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, integer linear programs, piecewise linear/quadratic objectives, feature maps specified with deep neural networks, etc.
Confidence Regions in Wasserstein Distributionally Robust Estimation
Wasserstein distributionally robust optimization estimators are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a certain distance (in a Wasserstein sense) from the underlying empirical measure.
Optimal Transport Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes
We consider optimal transport based distributionally robust optimization (DRO) problems with locally strongly convex transport cost functions and affine decision rules.
Optimization and Control Primary: 90C15, Secondary: 65K05, 90C47
Data-driven Optimal Cost Selection for Distributionally Robust Optimization
Recently, (Blanchet, Kang, and Murhy 2016, and Blanchet, and Kang 2017) showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) problems.
