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Daniel Kuhn

Publications associées (108)

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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

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

Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani

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

The Stochastic Time-Constrained Net Present Value Problem

Daniel Kuhn, Wolfram Wiesemann

The successful management of capital-intensive development and engineering projects requires a careful timing of the involved cash in- and outflows. To this end, the project management literature proposes to schedule the project activities so as to maximiz ...

Springer International Publishing2015

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 Markov Decision Processes

Daniel Kuhn, Wolfram Wiesemann

Markov decision processes (MDPs) are powerful tools for decision making in uncertain dynamic environments. However, the solutions of MDPs are of limited practical use because of their sensitivity to distributional model parameters, which are typically unkn ...

2013

Worst-Case Value at Risk of Nonlinear Portfolios

Daniel Kuhn

Portfolio optimization problems involving value at risk (VaR) are often computationally intractable and require complete information about the return distribution of the portfolio constituents, which is rarely available in practice. These difficulties are ...

2013

Distributionally Robust Joint Chance Constraints with Second-Order Moment Information

Daniel Kuhn

We develop tractable semidefinite programming based approximations for distributionally robust individual and joint chance constraints, assuming that only the first- and second-order moments as well as the support of the uncertain parameters are given. It ...

2013

A Polynomial-Time Solution Scheme for Quadratic Stochastic Programs

Daniel Kuhn

We consider quadratic stochastic programs with random recourse—a class of problems which is perceived to be computationally demanding. Instead of using mainstream scenario tree-based techniques, we reduce computational complexity by restricting the space o ...

2013

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