PyTorch-based End-to-End Predict-then-Optimize Tool

Contents:

  • Introduction
  • Installation
  • Tutorial
  • Module
  • Reference
  • API Reference
PyTorch-based End-to-End Predict-then-Optimize Tool
  • Reference
  • View page source

Reference

  • [1] Elmachtoub, A. N., & Grigas, P. (2021). Smart “predict, then optimize”. Management Science.

  • [2] Mandi, J., Stuckey, P. J., & Guns, T. (2020). Smart predict-and-optimize for hard combinatorial optimization problems. In Proceedings of the AAAI Conference on Artificial Intelligence.

  • [3] Vlastelica, M., Paulus, A., Musil, V., Martius, G., & Rolínek, M. (2019). Differentiation of blackbox combinatorial solvers. arXiv preprint arXiv:1912.02175.

  • [4] 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.

  • [5] 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.

  • [6] 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.

  • [7] Sahoo, S. S., Paulus, A., Vlastelica, M., Musil, V., Kuleshov, V., & Martius, G. (2022). Backpropagation through combinatorial algorithms: Identity with projection works. arXiv preprint arXiv:2205.15213.

Previous Next

© Copyright 2021, Bo Tang.

Built with Sphinx using a theme provided by Read the Docs.