Generalized Decision Focused Learning under Imprecise Uncertainty--Theoretical Study

Mulamba, Maxime and Mandi, Jayanta and Diligenti, Michelangelo and Lombardi, Michele and Bucarey, Victor and Guns, Tias, Contrastive Losses and Solution Caching for Predict-and-Optimize, IJCAI 2021

2021

Earlier work this paper cites.

Shah, S., et al. Decision-Focused Learning without Differentiable Optimisation: Learning Locally Optimized Decision Losses. NeurIPS, 2022

2022

Earlier work this paper cites.

Mandi, J., et al. Decision-Focused Learning Through the Lens of Learning to Rank. ICML, 2022

2022

Earlier work this paper cites.

Elmachtoub, A. N., & Grigas, P. Smart Predict-and-Optimize. Management Science, 2022

2022

Earlier work this paper cites.

Xi Lu and Shiwei Xia and Wei Gu and Ka Wing Chan, A model for balance responsible distribution systems with energy storage to achieve coordinated load shifting and uncertainty mitigation, Energy 2022

2022

Cited alongside, same era.

Jana Ksciuk and Stefan Kuhlemann and Kevin Tierney and Achim Koberstein, Uncertainty in maritime ship routing and scheduling: A Literature review, European Journal of Operational Research 2023

2023

Cited alongside, same era.

J. Mandi, J. Kotary, S. Berden, M. Mulamba, V. Bucarey, T. Guns, F. Fioretto (2024) Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities https://ar5iv.org/abs/2307.13565

Original

2024

Cited alongside, same era.

Schutte, N. J., Postek, K. S., & Yorke-Smith, N. (2024). Robust Losses for Decision-Focused Learning. In K. Larson (Ed.), Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 4868-4875) https://doi.org/10.24963/ijcai.2024/538

2024

Cited alongside, same era.

Schutte, N., et al. Robust Losses for Decision-Focused Learning. IJCAI-24 Proceedings, 2024

2024

Later among the works it cites.

James Kotary and Vincenzo Di Vito Francesco and Jacob K Christopher and Pascal Van Hentenryck and Ferdinando Fioretto, Predict-then-Optimize via Learning to Optimize from Features, ICLR 2024

2024

Later among the works it cites.

H. Jeon, H. Bae, M. Park, C. Kim, W. C. Kim (2025) Locally Convex Global Loss Network for Decision-Focused Learning https://ar5iv.org/pdf/2403.01875

2025

Closest in time.

Guangle Song and Tianlong Zhao and Xiang Ma and Peiguang Lin and Chaoran Cui, Reinforcement learning-based portfolio optimization with deterministic state transition, Information Sciences 2025

2025

Closest in time.