REVIEW 8 cited by
Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task. Recent literature considers integrating the estimation and optimization processes by selecting model parameters that lead to the best empirical objective performance. This integrated approach, which we call integrated-estimation-optimization (IEO), can be readily shown to outperform simple estimate-then-optimize (ETO) when the model is misspecified. In this paper, we show that a reverse behavior appears when the model class is well-specified and there is sufficient data. Specifically, for a general class of nonlinear stochastic optimization problems, we show that simple ETO outperforms IEO asymptotically when the model class covers the ground truth, in the strong sense of stochastic dominance of the regret. Namely, the entire distribution of the regret, not only its mean or other moments, is always better for ETO compared to IEO. Our results also apply to constrained, contextual optimization problems where the decision depends on observed features. Whenever applicable, we also demonstrate how standard sample average approximation (SAA) performs the worst when the model class is well-specified in terms of regret, and best when it is misspecified. Finally, we provide experimental results to support our theoretical comparisons and illustrate when our insights hold in finite-sample regimes and under various degrees of misspecification.
Forward citations
Cited by 8 Pith papers
-
Integrated Learning and Robust Optimization
A new framework, ILRO, trains cost predictors through a robust linear program, with a convex surrogate and consistency and convergence guarantees under stated conditions.
-
End-to-End Fairness Optimization with Fair Decision-Focused Learning
Jointly optimizing prediction accuracy, prediction disparity, and decision regret during training yields fairer prediction-informed resource allocations, with closed-form decision Jacobians for α-fair allocation problems.
-
Weak-to-Strong Learning in Decision Making
Weak-to-strong training with pseudo-distributions can improve downstream decision risk over strong-only training when labels are scarce, unlabeled data are abundant, and weak/strong feature overlap is small.
-
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
Prediction sets paired with max-min decisions are a sufficient statistic for value-at-risk optimizing agents, and the RAC algorithm builds such sets with distribution-free coverage.
-
Pessimistic bilevel optimization approach for decision-focused learning
A branch-and-cut method minimizes the pessimistic IEO regret loss directly for 0-1 combinatorial decision-focused learning, avoiding the need for a convex hull.
-
DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with Limited Data
Decision-Focused Fine-tuning corrects any backbone predictor's outputs within a bounded trust region using a residual scaling layer, improving decision regret on predict-then-optimize tasks.
-
Toward Decision-Oriented Prognostics: An Integrated Estimate-Optimize Framework for Predictive Maintenance
Training predictive maintenance models on downstream maintenance cost instead of prediction accuracy alone lowers average maintenance regret in turbofan experiments, by up to about 22% in the long-term setting.
-
Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact
The paper outlines a research vision for using LLM-based meta-agents to automate problem formulation, solution design, and evaluation in AI for social impact.
Discussion (0). Continue with ORCID to comment.