Scalable Decision Focused Learning via Online Trainable Surrogates

• URL: http://arxiv.org/abs/2512.03861v1
• Date: Wed, 03 Dec 2025 15:09:21 GMT
• Title: Scalable Decision Focused Learning via Online Trainable Surrogates
• Authors: Gaetano Signorelli, Michele Lombardi,
• Abstract summary: We propose an acceleration method based on replacing costly loss function evaluations with an efficient surrogate.<n>Unlike previously defined surrogates, our approach relies on unbiased estimators reducing the risk of spurious local optima.<n>In our results, the method reduces costly inner solver calls, with a solution quality comparable to other state-of-the-art techniques.
• Score: 9.624413875440233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decision support systems often rely on solving complex optimization problems that may require to estimate uncertain parameters beforehand. Recent studies have shown how using traditionally trained estimators for this task can lead to suboptimal solutions. Using the actual decision cost as a loss function (called Decision Focused Learning) can address this issue, but with a severe loss of scalability at training time. To address this issue, we propose an acceleration method based on replacing costly loss function evaluations with an efficient surrogate. Unlike previously defined surrogates, our approach relies on unbiased estimators reducing the risk of spurious local optima and can provide information on its local confidence allowing one to switch to a fallback method when needed. Furthermore, the surrogate is designed for a black-box setting, which enables compensating for simplifications in the optimization model and account- ing for recourse actions during cost computation. In our results, the method reduces costly inner solver calls, with a solution quality comparable to other state-of-the-art techniques.

Related papers

• Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems [54.916243942641444]
  Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications.<n>We study an edge-cloud-expert cascaded LLM-based knowledge system that supports decision-making through a question-and-answer pipeline.
  arXiv  Detail & Related papers  (2025-12-23T03:10:09Z)
• e1: Learning Adaptive Control of Reasoning Effort [88.51897900019485]
  Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning.<n>Users may prefer to allocate different amounts of reasoning effort depending on how they value output quality versus latency and cost.<n>We propose Adaptive Effort Control, a self-adaptive reinforcement learning method that trains models to use a user-specified fraction of tokens.
  arXiv  Detail & Related papers  (2025-10-30T23:12:21Z)
• Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems [2.365391421959969]
  This paper presents a transferable solution for optimal control problems with varying objectives using function encoder (FE) policies.<n>The proposed method learns a reusable set of neural basis functions that spans the control policy space.<n> Numerical experiments across diverse dynamics, dimensions, and cost structures show our method delivers near-optimal performance with minimal overhead.
  arXiv  Detail & Related papers  (2025-09-22T20:38:05Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Learning Joint Models of Prediction and Optimization [56.04498536842065]
  Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
  arXiv  Detail & Related papers  (2024-09-07T19:52:14Z)
• Memory-Enhanced Neural Solvers for Routing Problems [8.255381359612885]
  We present MEMENTO, an approach that leverages memory to improve the search of neural solvers at inference.<n>We validate its effectiveness on the Traveling Salesman and Capacitated Vehicle Routing problems, demonstrating its superiority over tree-search and policy-gradient fine-tuning.<n>We successfully train all RL auto-regressive solvers on large instances, and verify MEMENTO's scalability and data-efficiency.
  arXiv  Detail & Related papers  (2024-06-24T08:18:19Z)
• Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation [18.423687983628145]
  Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. This paper proposes a two-step approach integrating preference learning into the recourse generation problem.
  arXiv  Detail & Related papers  (2024-02-23T03:27:17Z)
• Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization [59.386153202037086]
  Predict-Then- framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This approach can be inefficient and requires handcrafted, problem-specific rules for backpropagation through the optimization step. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models.
  arXiv  Detail & Related papers  (2023-11-22T01:32:06Z)
• Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime [59.27851754647913]
  Predictive optimization is the precise modeling of many real-world applications, including energy cost-aware scheduling and budget allocation on advertising. We develop a modular framework to benchmark 11 existing PtO/PnO methods on 8 problems, including a new industrial dataset for advertising. Our study shows that PnO approaches are better than PtO on 7 out of 8 benchmarks, but there is no silver bullet found for the specific design choices of PnO.
  arXiv  Detail & Related papers  (2023-11-13T13:19:34Z)
• Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization Problems [0.0]
  We introduce a newimats for global optimization in scenarios where extensive evaluations of the cost function are expensive, inaccessible, or even prohibitive. The method, which we call Landscape-Sketch-and-Step (LSS), combines Machine Learning, Replica Optimization, and Reinforcement Learning techniques.
  arXiv  Detail & Related papers  (2023-09-14T01:53:45Z)
• Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize [57.22851616806617]
  We show that our method achieves state-of-the-art results in four domains from the literature. Our approach outperforms the best existing method by nearly 200% when the localness assumption is broken.
  arXiv  Detail & Related papers  (2023-05-26T11:17:45Z)
• Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution [30.161078999605152]
  We investigate an optimization problem in a queueing system where the service provider selects the optimal service fee p and service capacity mu.<n>We develop an online learning framework that automatically incorporates the parameter estimation errors in the solution prescription process.
  arXiv  Detail & Related papers  (2023-03-06T08:47:40Z)
• Contrastive Losses and Solution Caching for Predict-and-Optimize [19.31153168397003]
  We use a Noise Contrastive approach to motivate a family of surrogate loss functions. We address a major bottleneck of all predict-and-optimize approaches. We show that even a very slow growth rate is enough to match the quality of state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-10T19:09:12Z)

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.