Multi-task Predict-then-Optimize

Author:

Tang BoORCID,Khalil Elias B.ORCID

Publisher

Springer International Publishing

Reference34 articles.

1. Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., Kolter, J.Z.: Differentiable convex optimization layers. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, Curran Associates, Inc. (2019)

2. Ahuja, R.K., Orlin, J.B.: Inverse optimization. Oper. Res. 49(5), 771–783 (2001)

3. Amos, B., Kolter, J.Z.: OptNet: differentiable optimization as a layer in neural networks. In: International Conference on Machine Learning, pp. 136–145, PMLR (2017)

4. Bengio, Y.: Using a financial training criterion rather than a prediction criterion. Int. J. Neural Syst. 8(04), 433–443 (1997)

5. Berthet, Q., Blondel, M., Teboul, O., Cuturi, M., Vert, J.P., Bach, F.: Learning with differentiable perturbed optimizers. arXiv preprint arXiv:2002.08676 (2020)

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