mlsanityData to 7 Oct 2026

End-to-End Conformal Calibration for Optimization Under Uncertainty

arXiv 2409.20534 ↗First posted 30 Sep 2024cs.LG math.OCcited by 14

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Abstract (arXiv)

Machine learning can significantly improve performance for decision-making under uncertainty across a wide range of domains. However, ensuring robustness guarantees requires well-calibrated uncertainty estimates, which can be difficult to achieve with neural networks. Moreover, in high-dimensional settings, there may be many valid uncertainty estimates, each with its own performance profile - i.e., not all uncertainty is equally valuable for downstream decision-making. To address this problem, this paper develops an end-to-end framework to learn uncertainty sets for conditional robust optimization in a way that is informed by the downstream decision-making loss, with robustness and calibration guarantees provided by conformal prediction. In addition, we propose to represent general convex uncertainty sets with partially input-convex neural networks, which are learned as part of our framework. Our approach consistently improves upon two-stage estimate-then-optimize baselines on concrete applications in energy storage arbitrage and portfolio optimization.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 30 of its 58 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

30 of 58 references.

References · 30

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  3. 2019Conformalized Quantile Regressioncited by 620
  4. 2017OptNet: Differentiable Optimization as a Layer in Neural Networkscited by 588
  5. 2015Weight Uncertainty in Neural Networkscited by 554
  6. 2019Differentiable Convex Optimization Layerscited by 458
  7. 2015Deep Kernel Learningcited by 440
  8. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 417
  9. 2016Input Convex Neural Networkscited by 407
  10. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  11. 2017Smart "Predict, then Optimize"cited by 332
  12. 2022Conformal Risk Controlcited by 290
18 more
  1. 2018Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimizationcited by 220
  2. 2023Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunitiescited by 129
  3. 2021Learning Optimal Conformal Classifierscited by 108
  4. 2013Data-Driven Robust Optimizationcited by 84
  5. 2018Optimal Control Via Neural Networks: A Convex Approachcited by 82
  6. 2021Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learningcited by 81
  7. 2022Training Uncertainty-Aware Classifiers with Conformalized Deep Learningcited by 62
  8. 2021Conformal Uncertainty Sets for Robust Optimizationcited by 49
  9. 2023Conformal Contextual Robust Optimizationcited by 25
  10. 2023Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspectivecited by 19
  11. 2023Predict-then-Calibrate: A New Perspective of Robust Contextual LPcited by 17
  12. 2024End-to-end Conditional Robust Optimizationcited by 17
  13. 2023Learning Decision-Focused Uncertainty Sets in Robust Optimizationcited by 12
  14. 2023Disciplined Saddle Programmingcited by 6
  15. 2020Wasserstein Distributionally Robust Look-Ahead Economic Dispatchcited by 5
  16. 2021On Well-Structured Convex-Concave Saddle Point Problems and Variational Inequalities with Monotone Operatorscited by 4
  17. 2019A Chance-Constrained Stochastic Electricity Marketcited by 3
  18. 2020Nonparametric Estimation of Uncertainty Sets for Robust Optimizationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 7.

14 papers.

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  1. 2025Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agentscited by 37
  2. 2025Gen-DFL: Decision-Focused Generative Learning for Robust Decision Makingcited by 18
  3. 2024Utility-Directed Conformal Prediction: A Decision-Aware Framework for Actionable Uncertainty Quantificationcited by 11
  4. 2025Conformal Risk Training: End-to-End Optimization of Conformal Risk Controlcited by 11
  5. 2026Conformal Policy Controlcited by 5
  6. 2026Optimal Decision-Making Based on Prediction Setscited by 4
  7. 2024Fast and Reliable N-k Contingency Screening with Input-Convex Neural Networkscited by 2
  8. 2025Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraintscited by 2
  9. 2026Decision-calibrated prediction sets for robust power system operationscited by 2
  10. 2026Risk-Controlled Post-Processing of Decision Policiescited by 1
  11. 2026Learning Polyhedral Conformal Sets for Robust Optimizationcited by 1
  12. 2026Conformal Risk-Averse Decision Making with Action Conditional Guaranteecited by 1
2 more
  1. 2025Conformal Mixed-Integer Constraint Learning with Feasibility Guaranteescited by 0
  2. 2026Conformal Risk Sharing: Certified Cost Allocation with Participation Guaranteescited by 0