OPO: Making Decision-Focused Data Acquisition Decisions

• URL: http://arxiv.org/abs/2504.15062v1
• Date: Mon, 21 Apr 2025 12:41:35 GMT
• Title: OPO: Making Decision-Focused Data Acquisition Decisions
• Authors: Egon Peršak, Miguel F. Anjos,
• Abstract summary: We propose a model for making data acquisition decisions for variables in contextual optimisation problems.<n>We solve the data acquisition problem with well-defined constraints by learning a surrogate linear objective function.<n>We ablate the problem with a number of training modalities and demonstrate that the differentiable optimisation approach outperforms random search strategies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a model for making data acquisition decisions for variables in contextual stochastic optimisation problems. Data acquisition decisions are typically treated as separate and fixed. We explore problem settings in which the acquisition of contextual variables is costly and consequently constrained. The data acquisition problem is often solved heuristically for proxy objectives such as coverage. The more intuitive objective is the downstream decision quality as a result of data acquisition decisions. The whole pipeline can be characterised as an optimise-then-predict-then-optimise (OPO) problem. Analogously, much recent research has focused on how to integrate prediction and optimisation (PO) in the form of decision-focused learning. We propose leveraging differentiable optimisation to extend the integration to data acquisition. We solve the data acquisition problem with well-defined constraints by learning a surrogate linear objective function. We demonstrate an application of this model on a shortest path problem for which we first have to set a drone reconnaissance strategy to capture image segments serving as inputs to a model that predicts travel costs. We ablate the problem with a number of training modalities and demonstrate that the differentiable optimisation approach outperforms random search strategies.

Related papers

• Forecasting Outside the Box: Application-Driven Optimal Pointwise Forecasts for Stochastic Optimization [0.0]
  We present an integrated learning and optimization procedure that yields the best approximation of an unknown situation. Numerical results conducted with inventory problems from the literature as well as a bike-sharing problem with real data demonstrate that the proposed approach performs well.
  arXiv  Detail & Related papers  (2024-11-05T21:54:50Z)
• Differentiation of Multi-objective Data-driven Decision Pipeline [34.577809430781144]
  Real-world scenarios frequently involve multi-objective data-driven optimization problems. Traditional two-stage methods apply a machine learning model to estimate problem coefficients, followed by invoking a solver to tackle the predicted optimization problem. Recent efforts have focused on end-to-end training of predictive models that use decision loss derived from the downstream optimization problem.
  arXiv  Detail & Related papers  (2024-06-02T15:42:03Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• Experiment Planning with Function Approximation [49.50254688629728]
  We study the problem of experiment planning with function approximation in contextual bandit problems. We propose two experiment planning strategies compatible with function approximation. We show that a uniform sampler achieves competitive optimality rates in the setting where the number of actions is small.
  arXiv  Detail & Related papers  (2024-01-10T14:40:23Z)
• 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)
• Data-Driven Offline Decision-Making via Invariant Representation Learning [97.49309949598505]
  offline data-driven decision-making involves synthesizing optimized decisions with no active interaction. A key challenge is distributional shift: when we optimize with respect to the input into a model trained from offline data, it is easy to produce an out-of-distribution (OOD) input that appears erroneously good. In this paper, we formulate offline data-driven decision-making as domain adaptation, where the goal is to make accurate predictions for the value of optimized decisions.
  arXiv  Detail & Related papers  (2022-11-21T11:01:37Z)
• Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization [12.610576072466895]
  We consider the inverse problem where we use prior decision data to uncover the underlying decision-making process. This statistical learning problem is referred to as data-driven inverse optimization. We propose an efficient block coordinate descent-based algorithm to solve large problem instances.
  arXiv  Detail & Related papers  (2022-10-27T12:52:56Z)
• Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning [52.74071439183113]
  We study the predict-then-optimize framework in the context of sequential decision problems (formulated as MDPs) solved via reinforcement learning. Two significant computational challenges arise in applying decision-focused learning to MDPs.
  arXiv  Detail & Related papers  (2021-06-06T23:53:31Z)
• 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)
• Dynamic Federated Learning [57.14673504239551]
  Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm.
  arXiv  Detail & Related papers  (2020-02-20T15:00:54Z)

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.