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The field of Contextual Optimization (CO) integrates machine learning and optimization to solve decision making problems under uncertainty.
Robust convex optimization
Aharon Ben-Tal and Arkadi Nemirovski · 1998
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A trust region method based on interior point techniques for nonlinear programming
Richard H Byrd, Jean Charles Gilbert, and Jorge Nocedal · 2000
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
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Nonparametric density estimation for stochastic optimization with an observable state variable
Lauren Hannah, Warren Powell, and David Blei · 2010
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
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Deriving robust counterparts of nonlinear uncertain inequalities
Aharon Ben-Tal, Dick Den Hertog, and Jean-Philippe Vial · 2015
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Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
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Task-based end-to-end model learning in stochastic optimization
Priya Donti, Brandon Amos, and J Zico Kolter · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Auto-differentiating linear algebra
Matthias Seeger, Asmus Hetzel, Zhenwen Dai, Eric Meissner, and Neil D Lawrence · 2017
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Learning enabled optimization: Towards a fusion of statistical learning and stochastic programming
Suvrajeet Sen and Yunxiao Deng · 2018
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The limits of distribution-free conditional predictive inference
Rina Barber, Emmanuel Candès, Aaditya Ramdas, and Ryan Tibshirani · 2020
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From predictive to prescriptive analytics
Dimitris Bertsimas and Nathan Kallus · 2020
Cited alongside, same era.
A systematic review of algorithm aversion in augmented decision making
Jason W Burton, Mari-Klara Stein, and Tina Blegind Jensen · 2020
Cited alongside, same era.
Deep implicit layers tutorial - neural ODEs, deep equilibrium models, and beyond
David Duvenaud, J. Zico Kolter, and Matthew Johnson · 2020
Cited alongside, same era.
Smart predict-and-optimize for hard combinatorial optimization problems
Jayanta Mandi, Peter J Stuckey, Tias Guns, et al · 2020
Cited alongside, same era.
With malice toward none: Assessing uncertainty via equalized coverage
Yaniv Romano, Rina Foygel Barber, Chiara Sabatti, and Emmanuel Candès · 2020
Cited alongside, same era.
Ellipsoidal conformal inference for multi-target regression
Soundouss Messoudi, Sébastien Destercke, and Sylvain Rousseau · 2022
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Covariance prediction via convex optimization
Shane Barratt and Stephen Boyd · 2023
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Contextual uncertainty sets in robust linear optimization
Rafael Blanquero, Emilio Carrizosa, and Nuria Gómez-Vargas · 2023
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Efficient differentiable quadratic programming layers: an admm approach
Andrew Butler and Roy H Kwon · 2023
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Distributionally robust end-to-end portfolio construction
Giorgio Costa and Garud N Iyengar · 2023
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Conformal prediction with conditional guarantees, 2023
Isaac Gibbs, John J. Cherian, and Emmanuel J. Candès · 2023
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Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al · 2021
Cited alongside, same era.
Conformal uncertainty sets for robust optimization
Chancellor Johnstone and Bruce Cox · 2021
Cited alongside, same era.
A predictive prescription using minimum volume k-nearest neighbor enclosing ellipsoid and robust optimization
Shunichi Ohmori · 2021
Cited alongside, same era.
Efficient and modular implicit differentiation
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, and Jean-Philippe Vert · 2022
Cited alongside, same era.
Data-driven conditional robust optimization
Abhilash Reddy Chenreddy, Nymisha Bandi, and Erick Delage · 2022
Cited alongside, same era.
Smart “predict, then optimize”
Adam N Elmachtoub and Paul Grigas · 2022
Cited alongside, same era.
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G Giovanis, Lohit Vandanapu, and Michael D Shields · 2022
Cited alongside, same era.
Residuals-based distributionally robust optimization with covariate information
Rohit Kannan, Güzin Bayraksan, and James R Luedtke · 2023
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Decision-focused learning: Foundations, state of the art, benchmark and future opportunities, 2023
Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, and Ferdinando Fioretto · 2023
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Conformal contextual robust optimization
Yash Patel, Sahana Rayan, and Ambuj Tewari · 2023
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A survey of contextual optimization methods for decision making under uncertainty
Utsav Sadana, Abhilash Chenreddy, Erick Delage, Alexandre Forel, Emma Frejinger, and Thibaut Vidal · 2023
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Learning for robust optimization
Irina Wang, Cole Becker, Bart Van Parys, and Bartolomeo Stellato · 2023
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Predict-then-calibrate: A new perspective of robust contextual lp
Chunlin Sun, Linyu Liu, and Xiaocheng Li · 2024
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