Published in Mathematical Programming, 2022
We review some recent advances that highlight the difference that optimization can make in data-driven decision-making
Recommended citation: Baardman, L., Cristian, R., Perakis, G., Singhvi, D., Skali Lami, O., & Thayaparan, L. (2022). The role of optimization in some recent advances in data-driven decision-making. Mathematical Programming, 1-35. https://rdcu.be/cTvux
Published in AAAI-23 Main Track, 2022
We present a novel appoach in joint prediction and optimization by introducing a neural network architecture (ProjectNet) capable of approximately solving optimization problems.
Recommended citation: Cristian, R., Harsha, P., Perakis, G., Quanz, B. L., & Spantidakis, I. (2022). End-to-End Learning via Constraint-Enforcing Approximators for Linear Programs with Applications to Supply Chains.
Published in In Preparation, 2022
We propose a novel approach that directly recommends robust, near-optimal decisions based on data within the context of contextual stochastic optimization problems.
Recommended citation: Cristian, R., Perakis, G. (2022). Learning Robust Decisions for Contextual Stochastic Optimization Problems Directly from Data