ICML 2022spotlight20 citations
An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming
Jihwan Jeong, Parth Jaggi, Andrew Butler, Scott Sanner
Abstract
Predictive models are traditionally optimized independently of their use in downstream decision-based optimization. The ‘smart, predict then optimize’ (SPO) framework addresses this shortcoming by optimizing predictive models in order to
BibTeX
@InProceedings{pmlr-v162-jeong22a,
title = {An Exact Symbolic Reduction of Linear Smart {P}redict+{O}ptimize to Mixed Integer Linear Programming},
author = {Jeong, Jihwan and Jaggi, Parth and Butler, Andrew and Sanner, Scott},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {10053--10067},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/jeong22a/jeong22a.pdf},
url = {https://proceedings.mlr.press/v162/jeong22a.html},
abstract = {Predictive models are traditionally optimized independently of their use in downstream decision-based optimization. The ‘smart, predict then optimize’ (SPO) framework addresses this shortcoming by optimizing predictive models in order to