Daniel Kuhn
Daniel Kuhn
Professor of Operations Research
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Cited by
Cited by
Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations
P Mohajerin Esfahani, D Kuhn
Mathematical Programming 171 (1), 115-166, 2018
Distributionally robust convex optimization
W Wiesemann, D Kuhn, M Sim
Operations research 62 (6), 1358-1376, 2014
Distributionally robust joint chance constraints with second-order moment information
S Zymler, D Kuhn, B Rustem
Mathematical Programming 137, 167-198, 2013
Robust Markov Decision Processes
W Wiesemann, D Kuhn, B Rustem
Mathematics of Operations Research, 2010
Wasserstein distributionally robust optimization: Theory and applications in machine learning
D Kuhn, PM Esfahani, VA Nguyen, S Shafieezadeh-Abadeh
Operations research & management science in the age of analytics, 130-166, 2019
Distributionally robust logistic regression
S Shafieezadeh Abadeh, PM Mohajerin Esfahani, D Kuhn
Advances in neural information processing systems 28, 2015
Primal and dual linear decision rules in stochastic and robust optimization
D Kuhn, W Wiesemann, A Georghiou
Mathematical Programming 130, 177-209, 2011
Regularization via mass transportation
S Shafieezadeh-Abadeh, D Kuhn, PM Esfahani
Journal of Machine Learning Research 20 (103), 1-68, 2019
K-Adaptability in Two-Stage Robust Binary Programming
GA Hanasusanto, D Kuhn, W Wiesemann
Operations Research 63 (4), 877-891, 2015
Conic programming reformulations of two-stage distributionally robust linear programs over Wasserstein balls
GA Hanasusanto, D Kuhn
Operations Research 66 (3), 849-869, 2018
Distributionally robust control of constrained stochastic systems
BPG Van Parys, D Kuhn, PJ Goulart, M Morari
IEEE Transactions on Automatic Control 61 (2), 430-442, 2015
Generalized decision rule approximations for stochastic programming via liftings
A Georghiou, W Wiesemann, D Kuhn
Mathematical Programming 152, 301-338, 2015
A distributionally robust perspective on uncertainty quantification and chance constrained programming
GA Hanasusanto, V Roitch, D Kuhn, W Wiesemann
Mathematical Programming 151, 35-62, 2015
Data-driven chance constrained programs over Wasserstein balls
Z Chen, D Kuhn, W Wiesemann
Operations Research 72 (1), 410-424, 2024
Worst-case value at risk of nonlinear portfolios
S Zymler, D Kuhn, B Rustem
Management Science 59 (1), 172-188, 2013
Distributionally robust multi-item newsvendor problems with multimodal demand distributions
GA Hanasusanto, D Kuhn, SW Wallace, S Zymler
Mathematical Programming 152 (1), 1-32, 2015
Ambiguous joint chance constraints under mean and dispersion information
GA Hanasusanto, V Roitch, D Kuhn, W Wiesemann
Operations Research 65 (3), 751-767, 2017
From data to decisions: Distributionally robust optimization is optimal
BPG Van Parys, PM Esfahani, D Kuhn
Management Science 67 (6), 3387-3402, 2021
Data-driven inverse optimization with imperfect information
P Mohajerin Esfahani, S Shafieezadeh-Abadeh, GA Hanasusanto, ...
Mathematical Programming 167, 191-234, 2018
Maximizing the net present value of a project under uncertainty
W Wiesemann, D Kuhn, B Rustem
European Journal of Operational Research 202 (2), 356-367, 2010
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