Daniel Kuhn Wasserstein Distributionally Robust Optimization With Heterogeneous Data Sources

GitHub - Aaronkandel/Wasserstein-Robust-Optimization: Starter Pack For Distributionally Robust ...

GitHub - Aaronkandel/Wasserstein-Robust-Optimization: Starter Pack For Distributionally Robust ... Daniel kuhn wasserstein distributionally robust optimization with heterogeneous data sources robust optimization webinar 928 subscribers subscribed. This talk will highlight two recent advances in wasserstein dro. first, we will develop a principled approach to leveraging samples from heterogeneous data sources for making better decisions.

2: Comparison Of Robust Optimization With Distributionally Robust... | Download Scientific Diagram

2: Comparison Of Robust Optimization With Distributionally Robust... | Download Scientific Diagram Data driven distributionally robust optimization using the wasserstein metric daniel kuhn risk analytics and optimization chair École polytechnique fédérale de lausanne rao.epfl.ch erick’s notes i wish to warmly thank daniel kuhn for sharing this keynote presentation. By using a fair metric as the transportation cost function in computing the wasserstein distance, models are designed to deliver consistent performance across varied data distributions, ensuring similar individuals receive comparable outcomes, thus satisfying individual fairness. Using recent measure concentration results from statistics, we demonstrate that the optimal value of a distributionally robust optimization problem over a wasserstein ambiguity set provides an upper confidence bound on the out of sample cost of the worst case optimal decision. Risk analytics and optimization laboratory, École polytechnique fédérale de lausanne, lausanne ch 1015, switzerland [email protected] we study decision problems under uncertainty, where the decision maker has access to k data sources that carry biased information about the underlying risk factors. the biases are measured by the mismatch.

(PDF) Regularization For Wasserstein Distributionally Robust Optimization

(PDF) Regularization For Wasserstein Distributionally Robust Optimization Using recent measure concentration results from statistics, we demonstrate that the optimal value of a distributionally robust optimization problem over a wasserstein ambiguity set provides an upper confidence bound on the out of sample cost of the worst case optimal decision. Risk analytics and optimization laboratory, École polytechnique fédérale de lausanne, lausanne ch 1015, switzerland [email protected] we study decision problems under uncertainty, where the decision maker has access to k data sources that carry biased information about the underlying risk factors. the biases are measured by the mismatch. We study decision problems under uncertainty, where the decision maker has access to $k$ data sources that carry {\em biased} information about the underlying risk factors. This talk will highlight two recent advances in wasserstein dro. first, we will develop a principled approach to leveraging samples from heterogeneous data sources for making better decisions. For a comprehensive review of the theory and applications of robust optimization we refer to (ben tal and nemirovski 1998, 1999a, 2000, 2002, bertsimas and sim 2004, ben tal et al. 2009, bertsimas, brown and caramanis 2011, ben tal, den hertog and vial 2015a, bertsimas and den hertog 2022).

(PDF) Distributionally Robust Optimization With Wasserstein Metric For Multi-period Portfolio ...

(PDF) Distributionally Robust Optimization With Wasserstein Metric For Multi-period Portfolio ... We study decision problems under uncertainty, where the decision maker has access to $k$ data sources that carry {\em biased} information about the underlying risk factors. This talk will highlight two recent advances in wasserstein dro. first, we will develop a principled approach to leveraging samples from heterogeneous data sources for making better decisions. For a comprehensive review of the theory and applications of robust optimization we refer to (ben tal and nemirovski 1998, 1999a, 2000, 2002, bertsimas and sim 2004, ben tal et al. 2009, bertsimas, brown and caramanis 2011, ben tal, den hertog and vial 2015a, bertsimas and den hertog 2022).

Daniel Kuhn - Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources