Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Many predictive machine learning (ML) models are integrated within the context of a larger system as part of a key component for decision making processes. Concretely, the models are built first, then the model outputs are used to generate decision values separately. However, it is often the case that the prediction values that are trained independently of the optimization process produce sub-optimal solutions. In this paper, we propose a formulation for the Simultaneous Prediction and Optimization (SimPO) framework. This framework introduces the use of a joint weighted loss of a decision-driven predictive ML model and an optimization objective function, which is optimized end-to-end directly through gradient-based methods.
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Natalia Martinez Gil, Dhaval Patel, et al.
UAI 2024
Shubhi Asthana, Pawan Chowdhary, et al.
KDD 2021
Ademir Ferreira Da Silva, Levente Klein, et al.
INFORMS 2022