Daniel Kuhn · Wasserstein Distributionally Robust Optimization Theory And Applications In

Free Video: Wasserstein Distributionally Robust Optimization - Theory And Applications In ...

Free Video: Wasserstein Distributionally Robust Optimization - Theory And Applications In ... View a pdf of the paper titled wasserstein distributionally robust optimization: theory and applications in machine learning, by daniel kuhn and 3 other authors. Wasserstein distributionally robust optimization: theory and applications in machine learning abstract many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples.

(PDF) Shortfall-Based Wasserstein Distributionally Robust Optimization

(PDF) Shortfall-Based Wasserstein Distributionally Robust Optimization Intersections between control, learning and optimization 2020 "wasserstein distributionally robust optimization: theory and applications in machine learning" daniel kuhn École. Using the wasserstein metric, we construct a ball in the space of (multivariate and non discrete) probability distributions centered at the uniform distribution on the training samples, and we seek decisions that perform best in view of the worst case distribution within this wasser stein ball. Wasserstein distributionally robust optimization seeks data driven decisions that perform well under the most adverse distribution within a certain wasserstein distance from a nominal distribution constructed from the training samples. My research interests include reinforcement learning, machine learning, statistical signal processing and information theory. current research projects include: i am an associate professor with.

(PDF) Distributionally Robust Stochastic Optimization With Wasserstein Distance

(PDF) Distributionally Robust Stochastic Optimization With Wasserstein Distance Wasserstein distributionally robust optimization seeks data driven decisions that perform well under the most adverse distribution within a certain wasserstein distance from a nominal distribution constructed from the training samples. My research interests include reinforcement learning, machine learning, statistical signal processing and information theory. current research projects include: i am an associate professor with. In this tutorial we will argue that this approach has many conceptual and computational benefits. most prominently, the optimal decisions can often be computed by solving tractable convex optimization problems, and they enjoy rigorous out of sample and asymptotic consistency guarantees. Wasserstein distributionally robust optimization seeks data driven decisions that perform well under the most adverse distribution within a certain wasserstein distance from a nominal distribution constructed from the training samples. Dro is rooted in statistics, operations research and control theory, and recent research has uncovered its deep connections to regularization techniques and adversarial training in machine learning. this survey presents the key findings of the field in a unified and self contained manner. 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).

Wasserstein Distributionally Robust Control Barrier Function Using Conditional Value-at-Risk ...

Wasserstein Distributionally Robust Control Barrier Function Using Conditional Value-at-Risk ... In this tutorial we will argue that this approach has many conceptual and computational benefits. most prominently, the optimal decisions can often be computed by solving tractable convex optimization problems, and they enjoy rigorous out of sample and asymptotic consistency guarantees. Wasserstein distributionally robust optimization seeks data driven decisions that perform well under the most adverse distribution within a certain wasserstein distance from a nominal distribution constructed from the training samples. Dro is rooted in statistics, operations research and control theory, and recent research has uncovered its deep connections to regularization techniques and adversarial training in machine learning. this survey presents the key findings of the field in a unified and self contained manner. 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: Theory and Applications in Machi..."