[20] C. R. Harvey, J. C. Liechty, M. W. Liechty, and P. Muller. Portfolio selection with higher moments. Quantitative Finance, 10:469-485, 2010.
[21] X. Hu, J. C. Lee, J. H. Lee, and A. Z. Zhong. Branch & learn for recursively and iteratively solvable problems in predict+optimize. arXiv:2205.01672, May 01 2022.
[22] Y.-h. Kao, B. Roy, and X. Yan. Directed regression. In Advances in Neural Information Processing System, 2022.
[23] E. Khalil, H. Dai, Y. Zhang, B. Dilkina, and L. Song. Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30, 2017.
[24] V. R. Konda and J. N. Tsitsiklis. Actor-critic algorithms. In Advances in Neural Information Processing System, 1999.
[25] B. Korte and J. Vygen. Combinatorial Optimization: Theory and Algorithms. Number 21 in Algorithms and Combinatorics. Springer-Verlag, sixth edition, 2018.
[26] S. Lim, C. Sommer, E. Nikolova, and D. Rus. Practical route planning under delay uncertainty: Stochastic shortest path queries. In Robotics: Science and Systems VIII, pages 249-256, 2012. doi: 10.15607/RSS.2012.VIII.032.
[27] L.-J. Lin. Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning, 8:293--321, 1992.
[28] J. Mandi, P. J. Stuckey, T. Guns, et al. Smart predict-and-optimize for hard combinatorial optimization problems. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 1603–1610, 2020.
[29] J. Mandi, V. Bucarey, M. M. K. Tchomba, and T. Guns. Decision-focused learning: Through the lens of learning to rank. In International Conference on Machine Learning, pages 14935-14947. PMLR, 2022.
[30] H. Markowitz. Portfolio selection. J. of Finance, 7(1):77-91, Mar. 1952.
[31] M. Niepert, P. Minervini, and L. Franceschi. Implicit mle: backpropagating through discrete exponential family distributions. Advances in Neural Information Processing Systems, 34: 14567-14579, 2021.
[32] J. Nocedal and S. J. Wright. Numerical Optimization. Springer Series in Operations Research and Financial Engineering. Springer-Verlag, New York, second edition, 2006.
[33] A. Paulus, M. Rolínek, V. Musil, B. Amos, and G. Martius. Comboptnet: Fit the right np-hard problem by learning integer programming constraints. arXiv:2105.02343, May 12 2021.
[34] M. V. Pogančić, A. Paulus, V. Musil, G. Martius, and M. Rolinek. Differentiation of blackbox combinatorial solvers. In Proc. of the 8th Int. Conf. Learning Representations (ICLR 2020), Addis Ababa, Ethiopia, Apr. 26–30 2020.
[35] Quandl. Wiki various end-of-day data, 2022. URL https://www.quandl.com/data/WIKI.
[36] N. V. Sahinidis. BARON 21.1.13: Global Optimization of Mixed-Integer Nonlinear Programs, User's Manual, 2017.
[37] S. Shah, K. Wang, B. Wilder, A. Perrault, and M. Tambe. Decision-focused learning without decision-making: Learning locally optimized decision losses. In Advances in Neural Information Processing System, pages 1320-1332, 2022.
[38] B. Tang and E. B. Khalil. Pyepo: A pytorch-based end-to-end predict-then-optimize library for linear and integer programming. arXiv:2206.14234, June 28 2022.
[39] P.-W. Wang, P. Donti, B. Wilder, and Z. Kolter. Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. In International Conference on Machine Learning, pages 6545-6554. PMLR, 2019.