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Decision-focused learning (DFL) integrates predictive modeling and optimization by training predictors to optimize the downstream decision target rather than merely minimizing prediction error.
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 1938
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The Sample Average Approximation Method for Stochastic Discrete Optimization
Anton J. Kleywegt, Alexander Shapiro, and Tito Homem-de Mello · 2002
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Stochastic Convex Optimization
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2009
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Identifying risk issues and research advancements in supply chain risk management
Ou Tang and S. Nurmaya Musa · 2010
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2014
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A Guide to Sample Average Approximation
Sujin Kim, Raghu Pasupathy, and Shane G. Henderson · 2015
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Decision Making Under Uncertainty: Theory and Application
Mykel J. Kochenderfer, Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, Jason R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre, and John Vian · 2015
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ELBO surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
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OptNet: Differentiable Optimization as a Layer in Neural Networks
Brandon Amos and J. Zico Kolter · 2017
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Task-based End-to-end Model Learning in Stochastic Optimization
Priya L. Donti, Brandon Amos, and J. Zico Kolter · 2017
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From Predictive to Prescriptive Analytics
Dimitris Bertsimas and Nathan Kallus · 2018
Earlier work this paper cites.
Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Bryan Wilder, Bistra Dilkina, and Milind Tambe · 2019
Cited alongside, same era.
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, and Karthik Sridharan · 2020
Cited alongside, same era.
Smart “Predict, then Optimize”
Adam N. Elmachtoub and Paul Grigas · 2020
Cited alongside, same era.
Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Monte Carlo Gradient Estimation in Machine Learning
Shakir Mohamed, Mihaela Rosca, Michael Figurnov, and Andriy Mnih · 2020
Cited alongside, same era.
Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Diffusion Models for Black-Box Optimization
Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, and Aditya Grover · 2023
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Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali İrfan Mahmutoğulları, Maxime Mulamba, Allegra De Filippo, Tias Guns, and Michele Lombardi · 2023
Later among the works it cites.
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization
Zhiqing Sun and Yiming Yang · 2023
Later among the works it cites.
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, and Ferdinando Fioretto · 2024
Later among the works it cites.
Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data
Yafei Wang, Bo Pan, Mei Li, Jianya Lu, Lingchen Kong, Bei Jiang, and Linglong Kong · 2024
Later among the works it cites.
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NVAE: A Deep Hierarchical Variational Autoencoder
Arash Vahdat and Jan Kautz · 2020
Cited alongside, same era.
Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Improved Denoising Diffusion Probabilistic Models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon · 2021
Cited alongside, same era.
Decision-Focused Learning: Through the Lens of Learning to Rank
Jayanta Mandi, Victor Bucarey, Maxime Mulamba Ke Tchomba, and Tias Guns · 2022
Cited alongside, same era.
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses
Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, and Milind Tambe · 2022
Cited alongside, same era.
Locally Convex Global Loss Network for Decision-Focused Learning
Haeun Jeon, Hyunglip Bae, Minsu Park, Chanyeong Kim, and Woo Chang Kim · 2025
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Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Dongxia Wu, Haorui Wang, Aaron M. Ferber, Yian Ma, Carla P. Gomes, and Chao Zhang · 2025
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