Fetching the paper…
Reading the bibliography…
Predict-then-Optimize is a framework for using machine learning to perform decision-making under uncertainty.
Ohio Supercomputer Center
OSC. 1987 · 1987
Earlier work this paper cites.
Using a financial training criterion rather than a prediction criterion
Bengio, Y. 1997 · 1997
Earlier work this paper cites.
Mean-variance analysis in portfolio choice and capital markets
Markowitz, H. M.; and Todd, G. P. 2000 · 2000
Earlier work this paper cites.
Task-based end-to-end model learning in stochastic optimization
Donti, P.; Amos, B.; and Kolter, J. Z. 2017 · 2017
Earlier work this paper cites.
Cyclical learning rates for training neural networks
Smith, L. N. 2017 · 2017
Earlier work this paper cites.
Differentiable MPC for End-to-end Planning and Control
Amos, B.; Jimenez, I.; Sacks, J.; Boots, B.; and Kolter, J. Z. 2018 · 2018
Earlier work this paper cites.
Semi-Supervised Prediction-Constrained Topic Models
Hughes, M.; Hope, G.; Weiner, L.; McCoy, T.; Perlis, R.; Sudderth, E.; and Doshi-Velez, F. 2018 · 2018
Earlier work this paper cites.
Differentiable dynamic programming for structured prediction and attention
Mensch, A.; and Blondel, M. 2018 · 2018
Earlier work this paper cites.
Differentiable submodular maximization
Tschiatschek, S.; Sahin, A.; and Krause, A. 2018 · 2018
Earlier work this paper cites.
Differentiable convex optimization layers
Agrawal, A.; Amos, B.; Barratt, S.; Boyd, S.; Diamond, S.; and Kolter, J. Z. 2019 · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C. 2019 · 2019
Cited alongside, same era.
Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization
Wilder, B.; Dilkina, B.; and Tambe, M. 2019 · 2019
Cited alongside, same era.
End to end learning and optimization on graphs
Wilder, B.; Ewing, E.; Dilkina, B.; and Tambe, M. 2019 · 2019
Cited alongside, same era.
MIPaaL: Mixed integer program as a layer
Ferber, A.; Wilder, B.; Dilkina, B.; and Tambe, M. 2020 · 2020
Cited alongside, same era.
Automatically learning compact quality-aware surrogates for optimization problems
Wang, K.; Wilder, B.; Perrault, A.; and Tambe, M. 2020 · 2020
Contrastive Losses and Solution Caching for Predict-and-Optimize
Mulamba, M.; Mandi, J.; Diligenti, M.; Lombardi, M.; Bucarey, V.; and Guns, T. 2021 · 2021
Later among the works it cites.
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning
Wang, K.; Shah, S.; Chen, H.; Perrault, A.; Doshi-Velez, F.; and Tambe, M. 2021 · 2021
Later among the works it cites.
Decision-Aware Learning for Optimizing Health Supply Chains
Chung, T.-H.; Rostami, V.; Bastani, H.; and Bastani, O. 2022 · 2022
Later among the works it cites.
A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret
Lawless, C.; and Zhou, A. 2022 · 2022
Later among the works it cites.
WIKI Various End-Of-Day Data
Quandl. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Differentiable top-k with optimal transport
Xie, Y.; Dai, H.; Chen, M.; Dai, B.; Zhao, T.; Zha, H.; Wei, W.; and Pfister, T. 2020 · 2020
Cited alongside, same era.
Smart “predict, then optimize”
Elmachtoub, A. N.; and Grigas, P. 2021 · 2021
Cited alongside, same era.
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses
Shah, S.; Wang, K.; Wilder, B.; Perrault, A.; and Tambe, M. 2022 · 2022
Later among the works it cites.
Wang, K.; Verma, S.; Mate, A.; Shah, S.; Taneja, A.; Madhiwalla, N.; Hegde, A.; and Tambe, M. 2022 · 2022
Later among the works it cites.
A1 - Yahoo! Search Marketing Advertising Bidding Data, Version 1.0
Yahoo! 2007 · 2022
Later among the works it cites.