Smart predict-then-robustly-optimize

• URL: http://arxiv.org/abs/2607.21773v1
• Date: Thu, 23 Jul 2026 19:37:17 GMT
• Title: Smart predict-then-robustly-optimize
• Abstract summary: We propose and study a robust variant of the smart predict-then-optimize approach.<n>Our framework consistently yields significant performance improvements over standard methods.
• Score: 0.5161531917413708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose and study a robust variant of the smart predict-then-optimize approach that accounts for prediction shifts due to disturbance in the covariate feature space. While traditional integrated-learning-and-optimization models assume that side information is perfectly revealed, empirical data-driven features are frequently corrupted or noisy at the time of decision-making, leading to fragile operational policies. To bridge this gap, we integrate principles of robust optimization directly into the predictive-prescriptive pipeline via a smart predict-then-robustly optimize loss and establish a computationally tractable convex surrogate, designed to hedge against worst-case feature perturbations. On the theoretical front, we formalize the structural validity of this surrogate by proving its approximation error probability decays exponentially according to a sub-Gaussian concentration profile. Furthermore, we establish that under mild assumptions, the surrogate is Fisher consistent with high probability. We also prove necessary conditions under which our framework outperforms standard smart predict-then-optimize and maintain its superiority even when the standard method is equipped with regularized upstream predictions. Numerical experiments validate that our robust framework consistently yields significant performance improvements over standard methods, both in out-of-sample terms and in training stability.

Related papers

• Conformal Reliability: A New Evaluation Metric for Conditional Generation [62.761166941396844]
  Conditional generative models have recently remarkable success in various applications.<n>We propose a novel evaluation metric called reliability based on prediction set at a pre-specified confidence level.<n>We introduce Conformal ReLiability (CReL), a framework that can (i) construct a prediction set and (ii) accurately optimize the reliability score within the constructed prediction set.
  arXiv  Detail & Related papers  (2026-05-29T03:52:44Z)
• Possibilistic Predictive Uncertainty for Deep Learning [75.00607359322863]
  We introduce Dirichlet-approximated possibilistic posterior predictions (DAPPr)<n>DAPPr is a principled framework leveraging possibility theory.<n>This projection-and-approximation strategy yields a simple training objective with closed-form solutions.
  arXiv  Detail & Related papers  (2026-05-01T12:14:01Z)
• Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework [8.124770608442377]
  We formulate adaptive pooling as a statistical decision problem.<n>We propose a validation-driven framework that determines when and how specialization should be applied.<n> Experiments on large-scale traffic datasets demonstrate consistent improvements over strong baselines.
  arXiv  Detail & Related papers  (2026-04-15T11:35:32Z)
• Robust and Consistent Ski Rental with Distributional Advice [13.811651343801579]
  The ski rental problem is a canonical model for online decision-making under uncertainty.<n>We propose a framework that integrates distributional advice of unknown quality into both deterministic and randomized algorithms.<n>We show that our framework improves consistency significantly over existing point-prediction baselines while maintaining comparable robustness.
  arXiv  Detail & Related papers  (2026-03-31T04:04:21Z)
• Towards Anytime-Valid Statistical Watermarking [63.02116925616554]
  We develop the first e-value-based watermarking framework, Anchored E-Watermarking, that unifies optimal sampling with anytime-valid inference.<n>Our framework can significantly enhance sample efficiency, reducing the average token budget required for detection by 13-15% relative to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-19T18:32:26Z)
• Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models [52.48582333951919]
  We propose a dynamic framework designed to enhance alignment reliability by maximizing the Signal-to-Noise Ratio of policy updates.<n>SAGE (Stability-Aware Gradient Efficiency) integrates a coarse-grained curriculum mechanism that refreshes candidate pools based on model competence.<n> Experiments on multiple mathematical reasoning benchmarks demonstrate that SAGE significantly accelerates convergence and outperforms static baselines.
  arXiv  Detail & Related papers  (2026-02-01T12:56:10Z)
• Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints [9.77322868877488]
  We introduce a framework for distributionally robust optimal regularization.<n>We show how the resulting robust regularizers interpolate between computation of the training distribution and uniform priors.
  arXiv  Detail & Related papers  (2025-10-03T19:35:38Z)
• Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
  Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors.<n>We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves.
  arXiv  Detail & Related papers  (2024-09-27T21:46:42Z)
• Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization [29.24821214671497]
  Training machine learning and statistical models often involve optimizing a data-driven risk criterion. We propose a novel robust criterion by combining insights from Bayesian nonparametric (i.e., Dirichlet process) theory and a recent decision-theoretic model of smooth ambiguity-averse preferences. For practical implementation, we propose and study tractable approximations of the criterion based on well-known Dirichlet process representations.
  arXiv  Detail & Related papers  (2024-01-28T21:19:15Z)
• Causality-oriented robustness: exploiting general noise interventions [4.64479351797195]
  In this paper, we focus on causality-oriented robustness and propose Distributional Robustness via Invariant Gradients (DRIG)<n>DRIG exploits general noise interventions in training data for robust predictions against unseen interventions.<n>We show that our framework includes anchor regression as a special case, and that it yields prediction models that protect against more diverse perturbations.
  arXiv  Detail & Related papers  (2023-07-18T16:22:50Z)
• Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
  We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
  arXiv  Detail & Related papers  (2023-02-23T18:57:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.