Daniel Kuhn

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Publications associées (69)

A Distributionally Robust Perspective on Uncertainty Quantification and Chance Constrained Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability. A more ambitious goal is to actively influence the system so as to guarantee and mai ...

Springer Verlag2015

Generalized Decision Rule Approximations for Stochastic Programming via Liftings

Daniel Kuhn, Wolfram Wiesemann

Stochastic programming provides a versatile framework for decision-making under uncertainty, but the resulting optimization problems can be computationally demanding. It has recently been shown that, primal and dual linear decision rule approximations can ...

Springer Verlag2015

K-Adaptability in Two-Stage Robust Binary Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Over the last two decades, robust optimization has emerged as a computationally attractive approach to formulate and solve single-stage decision problems affected by uncertainty. More recently, robust optimization has been successfully applied to multi-sta ...

Informs2015

Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions

Daniel Kuhn, Grani Adiwena Hanasusanto

We present a risk-averse multi-dimensional newsvendor model for a class of products whose demands are strongly correlated and subject to fashion trends that are not fully understood at the time when orders are placed. The demand distribution is known to be ...

Springer Verlag2015

Distributionally Robust Logistic Regression

Peyman Mohajerin Esfahani, Daniel Kuhn, Soroosh Shafieezadeh Abadeh

This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ...

2015

Interdiction Games on Markovian PERT Networks

Daniel Kuhn, Eli Gutin, Wolfram Wiesemann

In a stochastic interdiction game a proliferator aims to minimize the expected duration of a nuclear weapons development project, and an interdictor endeavors to maximize the project duration by delaying some of the project tasks. We formulate static and d ...

Informs2015

Financial Optimization: Optimization Paradigms and Financial Planning under Uncertainty

Daniel Kuhn

Springer2015

Distributionally Robust Convex Optimization

Daniel Kuhn, Wolfram Wiesemann

Distributionally robust optimization is a paradigm for decision-making under uncertainty where the uncertain problem data is governed by a probability distribution that is itself subject to uncertainty. The distribution is then assumed to belong to an ambi ...

2014

Robust Data-Driven Dynamic Programming

Daniel Kuhn, Grani Adiwena Hanasusanto

In stochastic optimal control the distribution of the exogenous noise is typically unknown and must be inferred from limited data before dynamic programming (DP)-based solution schemes can be applied. If the conditional expectations in the DP recursions ar ...

2013

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