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Problem definition: A key challenge in supervised learning is data scarcity, which can cause prediction models to overfit to the training data and perform poorly out of sample.
Geometric Algorithms and Combinatorial Optimization
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The Markowitz optimization enigma: Is ‘optimized’optimal?
Michaud, R. O. (1989) · 1989
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Convex optimization
Boyd, S. and L. Vandenberghe (2004) · 2004
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The optimizer’s curse: Skepticism and postdecision surprise in decision analysis
Smith, J. E. and R. L. Winkler (2006) · 2006
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Robust Optimization
Ben-Tal, A., L. E. Ghaoui, and A. Nemirovski (2009) · 2009
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Convex optimization theory
Bertsekas, D. (2009) · 2009
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Hastie, T., R. Tibshirani, and J. Friedman (2009) · 2009
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A faster cutting plane method and its implications for combinatorial and convex optimization
Lee, Y. T., A. Sidford, and S. C.-W. Wong (2015) · 2015
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Distributionally robust logistic regression
Shafieezadeh-Abadeh, S., P. Mohajerin Esfahani, and D. Kuhn (2015) · 2015
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An analytics approach to designing combination chemotherapy regimens for cancer
Bertsimas, D., A. O’Hair, S. Relyea, and J. Silberholz (2016) · 2016
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Analytics for an online retailer: Demand forecasting and price optimization
Ferreira, K. J., B. H. A. Lee, and D. Simchi-Levi (2016) · 2016
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The UCI machine learning repository
Kelly, M., R. Longjohn, and K. Nottingham (2017) · 2017
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Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations
Mohajerin Esfahani, P. and D. Kuhn (2018) · 2018
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Ban, G.-Y. and C. Rudin (2019) · 2019
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Travel time estimation in the age of big data
Bertsimas, D., A. Delarue, P. Jaillet, and S. Martin (2019) · 2019
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Robust Wasserstein profile inference and applications to machine learning
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Quantifying distributional model risk via optimal transport
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Wasserstein distributionally robust optimization: Theory and applications in machine learning
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Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization
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Regularization via mass transportation
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Rahimian, H. and S. Mehrotra (2022) · 2022
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Overbooked and overlooked: Machine learning and racial bias in medical appointment scheduling
Samorani, M., S. L. Harris, L. G. Blount, H. Lu, and M. A. Santoro (2022) · 2022
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Wasserstein logistic regression with mixed features
Selvi, A., M. Belbasi, M. Haugh, and W. Wiesemann (2022) · 2022
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Alley, M., M. Biggs, R. Hariss, C. Herrmann, M. L. Li, and G. Perakis (2023) · 2023
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Online decision making with high-dimensional covariates
Bastani, H. and M. Bayati (2020) · 2020
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From predictive to prescriptive analytics
Bertsimas, D. and N. Kallus (2020) · 2020
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Robust and Adaptive Optimization
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Wasserstein distributionally robust optimization and variation regularization
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Distributionally robust optimization
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