Citation and References
+++++++++++++++++++++++

If you use ``PyEPO`` in your research, please cite:

.. code-block:: bibtex

   @article{tang2024,
     title={PyEPO: a PyTorch-based end-to-end predict-then-optimize library for linear and integer programming},
     author={Tang, Bo and Khalil, Elias B},
     journal={Mathematical Programming Computation},
     issn={1867-2957},
     doi={10.1007/s12532-024-00255-x},
     year={2024},
     month={July},
     publisher={Springer}
   }

If you use the ``CaVE`` loss, please also cite:

.. code-block:: bibtex

   @inproceedings{tang2024cave,
     title={CaVE: A Cone-Aligned Approach for Fast Predict-then-Optimize with Binary Linear Programs},
     author={Tang, Bo and Khalil, Elias B},
     booktitle={Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
     pages={193--210},
     year={2024},
     publisher={Springer}
   }


References
==========

* [1] `Elmachtoub, A. N., & Grigas, P. (2021). Smart "predict, then optimize". Management Science. <https://doi.org/10.1287/mnsc.2020.3922>`_
* [2] `Mandi, J., Stuckey, P. J., & Guns, T. (2020). Smart predict-and-optimize for hard combinatorial optimization problems. Proceedings of the AAAI Conference on Artificial Intelligence. <https://doi.org/10.1609/aaai.v34i02.5521>`_
* [3] `Vlastelica, M., Paulus, A., Musil, V., Martius, G., & Rolinek, M. (2020). Differentiation of blackbox combinatorial solvers. In International Conference on Learning Representations. <https://openreview.net/forum?id=BkevoJSYPB>`_
* [4] `Sahoo, S. S., Paulus, A., Vlastelica, M., Musil, V., Kuleshov, V., & Martius, G. (2023). Backpropagation through combinatorial algorithms: Identity with projection works. In International Conference on Learning Representations. <https://openreview.net/forum?id=JZMR727O29>`_
* [5] `Berthet, Q., Blondel, M., Teboul, O., Cuturi, M., Vert, J. P., & Bach, F. (2020). Learning with differentiable perturbed optimizers. Advances in Neural Information Processing Systems, 33, 9508-9519. <https://papers.nips.cc/paper/2020/hash/6bb56208f672af0dd65451f869fedfd9-Abstract.html>`_
* [6] `Dalle, G., Baty, L., Bouvier, L., & Parmentier, A. (2022). Learning with Combinatorial Optimization Layers: a Probabilistic Approach. arXiv preprint arXiv:2207.13513. <https://arxiv.org/abs/2207.13513>`_
* [7] `Mulamba, M., Mandi, J., Diligenti, M., Lombardi, M., Bucarey, V., & Guns, T. (2021). Contrastive losses and solution caching for predict-and-optimize. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. <https://www.ijcai.org/proceedings/2021/390>`_
* [8] `Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Decision-focused learning: through the lens of learning to rank. Proceedings of the 39th International Conference on Machine Learning. <https://proceedings.mlr.press/v162/mandi22a.html>`_
* [9] `Niepert, M., Minervini, P., & Franceschi, L. (2021). Implicit MLE: backpropagating through discrete exponential family distributions. Advances in Neural Information Processing Systems, 34, 14567-14579. <https://proceedings.neurips.cc/paper_files/paper/2021/hash/7a430339c10c642c4b2251756fd1b484-Abstract.html>`_
* [10] `Minervini, P., Franceschi, L., & Niepert, M. (2023). Adaptive perturbation-based gradient estimation for discrete latent variable models. Proceedings of the AAAI Conference on Artificial Intelligence. <https://ojs.aaai.org/index.php/AAAI/article/view/26103>`_
* [11] `Gupta, V., & Huang, M. (2024). Decision-Focused Learning with Directional Gradients. Advances in Neural Information Processing Systems, 37. <https://proceedings.neurips.cc/paper_files/paper/2024/hash/907a9fb75a408f6c3a2ae1bf84c39e44-Abstract-Conference.html>`_
* [12] `Schutte, N., Postek, K., & Yorke-Smith, N. (2024). Robust Losses for Decision-Focused Learning. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. <https://www.ijcai.org/proceedings/2024/538>`_
* [13] `Tang, B., & Khalil, E. B. (2024). CaVE: A Cone-Aligned Approach for Fast Predict-then-Optimize with Binary Linear Programs. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research (pp. 193-210). <https://link.springer.com/chapter/10.1007/978-3-031-60599-4_12>`_