Fetching the paper…
Reading the bibliography…
Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an end-to-end system.
A note on two problems in connexion with graphs
Dijkstra, E. (1959) · 1959
Earlier work this paper cites.
Backpropagation of unrolled solvers with folded optimization
Kotary, J., Dinh, M. H., and Fioretto, F. (2023b) · 1970
Earlier work this paper cites.
Information and exponential families: in statistical theory
Barndorff-Nielsen, O. (1978) · 1978
Earlier work this paper cites.
Multi-stage production-inventory systems
Goyal, S. K., and Gunasekaran, A. (1990) · 1990
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J. (1992) · 1992
Earlier work this paper cites.
Linear programming
Ignizio, J. P., and Cavalier, T. M. (1994) · 1994
Earlier work this paper cites.
Knapsack problems
Pisinger, D., and Toth, P. (1998) · 1998
Earlier work this paper cites.
CSPLib problem 059: Energy-cost aware scheduling
Simonis, H., O’Sullivan, B., Mehta, D., Hurley, B., and Cauwer, M. D. (1999) · 1999
Earlier work this paper cites.
An overview of statistical learning theory
Vapnik, V. (1999) · 1999
Earlier work this paper cites.
Optimizing search engines using clickthrough data
Joachims, T. (2002) · 2002
Earlier work this paper cites.
On solving linear programs with the ordered weighted averaging objective
Ogryczak, W., and Śliwiński, T. (2003) · 2003
Earlier work this paper cites.
Stochastic programming models
Ruszczyński, A., and Shapiro, A. (2003) · 2003
Earlier work this paper cites.
Optimization under uncertainty: state-of-the-art and opportunities
Sahinidis, N. V. (2004) · 2004
Earlier work this paper cites.
Optimal vehicle routing with real-time traffic information
Kim, S., Lewis, M. E., and White, C. C. (2005) · 2005
Earlier work this paper cites.
Where are the hard knapsack problems?
Pisinger, D. (2005) · 2005
Earlier work this paper cites.
Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach
Garlappi, L., Uppal, R., and Wang, T. (2006) · 2006
Earlier work this paper cites.
Risk bounds for statistical learning
Massart, P., and Nédélec, É. (2006) · 2006
Earlier work this paper cites.
Handbook of Constraint Programming
Rossi, F., van Beek, P., and Walsh, T. (Eds.). (2006) · 2006
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., and Li, H. (2007) · 2007
Earlier work this paper cites.
Advances in convex optimization: conic programming
Nemirovski, A. (2007) · 2007
Earlier work this paper cites.
Linear programming and network flows
Bazaraa, M. S., Jarvis, J. J., and Sherali, H. D. (2008) · 2008
Earlier work this paper cites.
Satisfiability solvers
Gomes, C. P., Kautz, H., Sabharwal, A., and Selman, B. (2008) · 2008
Earlier work this paper cites.
Graph implementations for nonsmooth convex programs
Grant, M. C., and Boyd, S. P. (2008) · 2008
Earlier work this paper cites.
Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T. (2008) · 2008
Earlier work this paper cites.
New multicategory boosting algorithms based on multicategory fisher-consistent losses
Zou, H., Zhu, J., and Hastie, T. (2008) · 2008
Earlier work this paper cites.
Robust optimization
Ben-Tal, A., El Ghaoui, L., and Nemirovski, A. (2009) · 2009
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Koller, D., and Friedman, N. (2009) · 2009
Earlier work this paper cites.
Theory and practice of uncertain programming
Liu, B., and Liu, B. (2009) · 2009
Earlier work this paper cites.
Active learning literature survey
Settles, B. (2009) · 2009
Earlier work this paper cites.
Distributionally robust optimization under moment uncertainty with application to data-driven problems
Delage, E., and Ye, Y. (2010) · 2010
Earlier work this paper cites.
Implicit differentiation by perturbation
Domke, J. (2010) · 2010
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M., and Hyvärinen, A. (2010) · 2010
Earlier work this paper cites.
Approximating electrical distribution networks via mixed-integer nonlinear programming
Lakhera, S., Shanbhag, U. V., and McInerney, M. K. (2011) · 2011
Earlier work this paper cites.
Perturb-and-map random fields: Using discrete optimization to learn and sample from energy models
Papandreou, G., and Yuille, A. L. (2011) · 2011
Earlier work this paper cites.
Generic methods for optimization-based modeling
Domke, J. (2012) · 2012
Earlier work this paper cites.
Properties of energy-price forecasts for scheduling
Ifrim, G., O’Sullivan, B., and Simonis, H. (2012) · 2012
Earlier work this paper cites.
A fast and simple algorithm for training neural probabilistic language models
Mnih, A., and Teh, Y. W. (2012) · 2012
Earlier work this paper cites.
Modern graph theory
Bollobás, B. (2013) · 2013
Earlier work this paper cites.
Learning deep structured semantic models for web search using clickthrough data
Huang, P., He, X., Gao, J., Deng, L., Acero, A., and Heck, L. P. (2013) · 2013
Earlier work this paper cites.
Convex Optimization
Boyd, S. P., and Vandenberghe, L. (2014) · 2014
Earlier work this paper cites.
Active graph matching for automatic joint segmentation and annotation of c. elegans
Kainmueller, D., Jug, F., Rother, C., and Myers, G. (2014) · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P., and Welling, M. (2014) · 2014
Earlier work this paper cites.
Multistage stochastic optimization
Pflug, G. C., and Pichler, A. (2014) · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
Earlier work this paper cites.
On distinguishability criteria for estimating generative models
Goodfellow, I. J. (2015) · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015) · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network.
Hinton, G., Vinyals, O., and Dean, J. (2015) · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P., and Ba, J. (2015) · 2015
Earlier work this paper cites.
Vehicle routing: Problems, methods, and applications
Toth, P., and Vigo, D. (2015) · 2015
Cited alongside, same era.
Perturbation techniques in online learning and optimization
Abernethy, J., Lee, C., and Tewari, A. (2016) · 2016
Cited alongside, same era.
Bridging the gap between predictive and prescriptive analytics-new optimization methodology needed
den Hertog, D., and Postek, K. (2016) · 2016
Cited alongside, same era.
On differentiating parameterized argmin and argmax problems with application to bi-level optimization.
Gould, S., Fernando, B., Cherian, A., Anderson, P., Cruz, R. S., and Guo, E. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Toward optimal energy management of microgrids via robust two-stage optimization
Gurobi optimizer reference manual
Gurobi Optimization, L. (2021) · 2021
Later among the works it cites.
End-to-end learning for prediction and optimization with gradient boosting
Konishi, T., and Fukunaga, T. (2021) · 2021
Later among the works it cites.
End-to-end constrained optimization learning: A survey
Kotary, J., Fioretto, F., Van Hentenryck, P., and Wilder, B. (2021) · 2021
Later among the works it cites.
Risk bounds and calibration for a smart predict-then-optimize method
Liu, H., and Grigas, P. (2021) · 2021
Later among the works it cites.
Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP
Mandi, J., Canoy, R., Bucarey, V., and Guns, T. (2021) · 2021
Later among the works it cites.
2021 amazon last mile routing research challenge: Data set
Merchán, D., Arora, J., Pachon, J., Konduri, K., Winkenbach, M., Parks, S., and Noszek, J. (2024) · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hu, W., Wang, P., and Gooi, H. B. (2016) · 2016
Cited alongside, same era.
From softmax to sparsemax: A sparse model of attention and multi-label classification
Martins, A., and Astudillo, R. (2016) · 2016
Cited alongside, same era.
Optnet: Differentiable optimization as a layer in neural networks
Amos, B., and Kolter, J. Z. (2017) · 2017
Cited alongside, same era.
Neural combinatorial optimization with reinforcement learning
Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2017) · 2017
Cited alongside, same era.
Differentiable learning of submodular models
Djolonga, J., and Krause, A. (2017) · 2017
Cited alongside, same era.
Task-based end-to-end model learning in stochastic optimization
Donti, P. L., Kolter, J. Z., and Amos, B. (2017) · 2017
Cited alongside, same era.
Warcraft ii open-source map editor
Guyomarch, J. (2017) · 2017
Cited alongside, same era.
Later among the works it cites.
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing
Monga, V., Li, Y., and Eldar, Y. C. (2021) · 2021
Later among the works it cites.
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.
Implicit mle: Backpropagating through discrete exponential family distributions
Niepert, M., Minervini, P., and Franceschi, L. (2021) · 2021
Later among the works it cites.
Comboptnet: Fit the right np-hard problem by learning integer programming constraints
Paulus, A., Rolínek, M., Musil, V., Amos, B., and Martius, G. (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.
The perils of learning before optimizing
Cameron, C., Hartford, J., Lundy, T., and Leyton-Brown, K. (2022) · 2022
Later among the works it cites.
Port selection for fluid antenna systems
Chai, Z., Wong, K.-K., Tong, K.-F., Chen, Y., and Zhang, Y. (2022) · 2022
Later among the works it cites.
Data-driven conditional robust optimization
Chenreddy, A. R., Bandi, N., and Delage, E. (2022) · 2022
Later among the works it cites.
Learning with combinatorial optimization layers: a probabilistic approach.
Dalle, G., Baty, L., Bouvier, L., and Parmentier, A. (2022) · 2022
Later among the works it cites.
Smart “predict, then optimize”
Elmachtoub, A. N., and Grigas, P. (2022) · 2022
Later among the works it cites.
A divide and conquer algorithm for predict+optimize with non-convex problems
Guler, A. U., Demirović, E., Chan, J., Bailey, J., Leckie, C., and Stuckey, P. J. (2022) · 2022
Later among the works it cites.
Branch & learn for recursively and iteratively solvable problems in predict+optimize
Hu, X., Lee, J. C., Lee, J. H., and Zhong, A. Z. (2022) · 2022
Later among the works it cites.
An exact symbolic reduction of linear smart Predict+Optimize to mixed integer linear programming
Jeong, J., Jaggi, P., Butler, A., and Sanner, S. (2022) · 2022
Later among the works it cites.
An exploration of poisoning attacks on data-based decision making
Kinsey, S. E., Tuck, W. W., Sinha, A., and Nguyen, T. H. (2023) · 2022
Later among the works it cites.
End-to-end learning for fair ranking systems
Kotary, J., Fioretto, F., Van Hentenryck, P., and Zhu, Z. (2022) · 2022
Later among the works it cites.
Decision-focused learning: Through the lens of learning to rank
Mandi, J., Bucarey, V., Tchomba, M. M. K., and Guns, T. (2022) · 2022
Later among the works it cites.
A solver-free framework for scalable learning in neural ilp architectures
Nandwani, Y., Ranjan, R., Mausam, and Singla, P. (2022) · 2022
Later among the works it cites.
Integrating prediction/estimation and optimization with applications in operations management
Qi, M., and Shen, Z.-J. (2022) · 2022
Later among the works it cites.
Electricity price prediction for energy storage system arbitrage: A decision-focused approach
Sang, L., Xu, Y., Long, H., Hu, Q., and Sun, H. (2022) · 2022
Later among the works it cites.
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.
Pairwise-comparison based semi-spo method for ship inspection planning in maritime transportation
Yang, Y., Yan, R., and Wang, H. (2022) · 2022
Later among the works it cites.
Decision-focused predictions via pessimistic bilevel optimization: a computational study.
Bucarey, V., Calderón, S., Muñoz, G., and Semet, F. (2023) · 2023
Later among the works it cites.
Data-driven optimization for last-mile delivery
Chu, H., Zhang, W., Bai, P., and Chen, Y. (2023) · 2023
Later among the works it cites.
Estimate-then-optimize versus integrated-estimation-optimization: A stochastic dominance perspective.
Elmachtoub, A. N., Lam, H., Zhang, H., and Zhao, Y. (2023) · 2023
Later among the works it cites.
Predicting wildlife trafficking routes with differentiable shortest paths
Ferber, A., Griffin, E., Dilkina, B., Keskin, B., and Gore, M. (2023a) · 2023
Later among the works it cites.
Surco: Learning linear surrogates for combinatorial nonlinear optimization problems
Ferber, A. M., Huang, T., Zha, D., Schubert, M., Steiner, B., Dilkina, B., and Tian, Y. (2023b) · 2023
Later among the works it cites.
Two-stage predict+optimize for milps with unknown parameters in constraints
Hu, X., Lee, J. C. H., and Lee, J. H. (2023b) · 2023
Later among the works it cites.
Modeling robustness in decision-focused learning as a stackelberg game
Johnson-Yu, S., Wang, K., Finocchiaro, J., Taneja, A., and Tambe, M. (2023) · 2023
Later among the works it cites.
Active learning in the predict-then-optimize framework: A margin-based approach.
Liu, M., Grigas, P., Liu, H., and Shen, Z.-J. M. (2023) · 2023
Later among the works it cites.
Adaptive perturbation-based gradient estimation for discrete latent variable models
Minervini, P., Franceschi, L., and Niepert, M. (2023) · 2023
Later among the works it cites.
Integrated conditional estimation-optimization.
Qi, M., Grigas, P., and Shen, Z.-J. M. (2023) · 2023
Later among the works it cites.
Backpropagation through combinatorial algorithms: Identity with projection works
Sahoo, S. S., Paulus, A., Vlastelica, M., Musil, V., Kuleshov, V., and Martius, G. (2023) · 2023
Later among the works it cites.
Score function gradient estimation to widen the applicability of decision-focused learning.
Silvestri, M., Berden, S., Mandi, J., İrfan Mahmutoğulları, A., Mulamba, M., Filippo, A. D., Guns, T., and Lombardi, M. (2023) · 2023
Later among the works it cites.
Predict-then-calibrate: A new perspective of robust contextual lp
Sun, C., Liu, L., and Li, X. (2023) · 2023
Later among the works it cites.
A smart predict-then-optimize method for targeted and cost-effective maritime transportation
Tian, X., Yan, R., Liu, Y., and Wang, S. (2023) · 2023
Later among the works it cites.
Electricity price forecasting based on order books: a differentiable optimization approach
Tschora, L., Guns, T., Pierre, E., Plantevit, M., and Robardet, C. (2023) · 2023
Later among the works it cites.
More than accuracy: end-to-end wind power forecasting that optimises the energy system
Wahdany, D., Schmitt, C., and Cremer, J. L. (2023) · 2023
Later among the works it cites.
LinSATNet: The positive linear satisfiability neural networks
Wang, R., Zhang, Y., Guo, Z., Chen, T., Yang, X., and Yan, J. (2023) · 2023
Later among the works it cites.
End-to-end learning for fair multiobjective optimization under uncertainty.
Dinh, M. H., Kotary, J., and Fioretto, F. (2024) · 2024
Later among the works it cites.
A survey of contextual optimization methods for decision-making under uncertainty
Sadana, U., Chenreddy, A., Delage, E., Forel, A., Frejinger, E., and Vidal, T. (2024) · 2024
Later among the works it cites.
Leaving the nest: Going beyond local loss functions for predict-then-optimize
Shah, S., Wilder, B., Perrault, A., and Tambe, M. (2024) · 2024
Later among the works it cites.