Searched for: subject%3A%22Distributionally%255C%2Brobust%255C%2Boptimization%22
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Nguyen, Viet Anh (author), Shafieezadeh-Abadeh, Soroosh (author), Kuhn, Daniel (author), Mohajerin Esfahani, P. (author)
We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator—that is, a measurable function of the observation—and a fictitious adversary...
journal article 2023
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Xia, D. (author), Ma, Jihui (author), Sharif Azadeh, S. (author), Zhang, Wenyi (author)
The collaborative design of the timetable and dynamic-capacity allocation plan of emerging modular vehicles (MVs) is a promising solution to the mismatch between supply and demand in public transportation studies; however, such efforts are subject to high-level dynamics and uncertainty inherent in operating environments. In this study, we...
journal article 2023
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Nguyen, Viet Anh (author), Kuhn, Daniel (author), Mohajerin Esfahani, P. (author)
We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst case (maximum) of Stein’s loss across all normal reference distributions within a...
journal article 2022
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van Parys, Bart P.G. (author), Mohajerin Esfahani, P. (author), Kuhn, Daniel (author)
We study stochastic programs where the decision maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transforms the data to an estimate of the expected cost function under the unknown data...
journal article 2021
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Uitterdijk, Niels (author)
This thesis presents a novel data-driven Fault Detection and Isolation algorithm for the public network of Electric Vehicle chargers of Tritium Ltd. Pty. The proposed solution is robust against marginal differences in data distribution as well as marginal changes over time due the state-of-the-art optimization techniques used. This is required...
master thesis 2020
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Shaéezadeh-Abadeh, Soroosh (author), Kuhn, Daniel (author), Mohajerin Esfahani, P. (author)
The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, overfitting is typically mitigated by adding regularization terms to the objective that...
journal article 2019
Searched for: subject%3A%22Distributionally%255C%2Brobust%255C%2Boptimization%22
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