Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model

• URL: http://arxiv.org/abs/2402.13530v2
• Date: Sat, 22 Jun 2024 21:24:15 GMT
• Title: Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model
• Authors: Lin An, Andrew A. Li, Benjamin Moseley, Gabriel Visotsky,
• Abstract summary: Online decision-makers often obtain predictions on future variables, such as arrivals, demands, and so on. Prediction accuracy is unknown to decision-makers a priori, hence blindly following the predictions can be harmful. We develop algorithms that utilize predictions in a manner that is robust to the unknown prediction accuracy.
• Score: 16.466711636334587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online decision-makers often obtain predictions on future variables, such as arrivals, demands, inventories, and so on. These predictions can be generated from simple forecasting algorithms for univariate time-series, all the way to state-of-the-art machine learning models that leverage multiple time-series and additional feature information. However, the prediction accuracy is unknown to decision-makers a priori, hence blindly following the predictions can be harmful. In this paper, we address this problem by developing algorithms that utilize predictions in a manner that is robust to the unknown prediction accuracy. We consider the Online Resource Allocation Problem, a generic model for online decision-making, in which a limited amount of resources may be used to satisfy a sequence of arriving requests. Prior work has characterized the best achievable performances when the arrivals are either generated stochastically (i.i.d.) or completely adversarially, and shown that algorithms exist which match these bounds under both arrival models, without ``knowing'' the underlying model. To this backdrop, we introduce predictions in the form of shadow prices on each type of resource. Prediction accuracy is naturally defined to be the distance between the predictions and the actual shadow prices. We tightly characterize, via a formal lower bound, the extent to which any algorithm can optimally leverage predictions (that is, to ``follow'' the predictions when accurate, and ``ignore'' them when inaccurate) without knowing the prediction accuracy or the underlying arrival model. Our main contribution is then an algorithm which achieves this lower bound. Finally, we empirically validate our algorithm with a large-scale experiment on real data from the retailer H&M.

Related papers

• Next Best View For Point-Cloud Model Acquisition: Bayesian Approximation and Uncertainty Analysis [2.07180164747172]
  This work adapts the point-net-based neural network for Next-Best-View (PC-NBV) It incorporates dropout layers into the model's architecture, thus allowing the computation of the uncertainty estimate associated with its predictions. The aim of the work is to improve the network's accuracy in correctly predicting the next best viewpoint.
  arXiv  Detail & Related papers  (2024-11-04T01:32:09Z)
• Towards Human-AI Complementarity with Prediction Sets [14.071862670474832]
  Decision support systems based on prediction sets have proven to be effective at helping human experts solve classification tasks. We show that the prediction sets constructed using conformal prediction are, in general, suboptimal in terms of average accuracy. We introduce a greedy algorithm that, for a large class of expert models and non-optimal scores, is guaranteed to find prediction sets that provably offer equal or greater performance.
  arXiv  Detail & Related papers  (2024-05-27T18:00:00Z)
• Algorithms with Prediction Portfolios [23.703372221079306]
  We study the use of multiple predictors for a number of fundamental problems, including matching, load balancing, and non-clairvoyant scheduling. For each of these problems we introduce new algorithms that take advantage of multiple predictors, and prove bounds on the resulting performance.
  arXiv  Detail & Related papers  (2022-10-22T12:58:07Z)
• Paging with Succinct Predictions [25.959849403994202]
  We study learning-augmented paging from the new perspective of requiring the least possible amount of predicted information. We develop algorithms for each of two setups that satisfy all three desirable properties of learning-augmented algorithms.
  arXiv  Detail & Related papers  (2022-10-06T09:26:34Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Non-Clairvoyant Scheduling with Predictions Revisited [77.86290991564829]
  In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a priori unknown processing requirements. We revisit this well-studied problem in a recently popular learning-augmented setting that integrates (untrusted) predictions in algorithm design. We show that these predictions have desired properties, admit a natural error measure as well as algorithms with strong performance guarantees.
  arXiv  Detail & Related papers  (2022-02-21T13:18:11Z)
• Robustification of Online Graph Exploration Methods [59.50307752165016]
  We study a learning-augmented variant of the classical, notoriously hard online graph exploration problem. We propose an algorithm that naturally integrates predictions into the well-known Nearest Neighbor (NN) algorithm.
  arXiv  Detail & Related papers  (2021-12-10T10:02:31Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Learning to Predict Trustworthiness with Steep Slope Loss [69.40817968905495]
  We study the problem of predicting trustworthiness on real-world large-scale datasets. We observe that the trustworthiness predictors trained with prior-art loss functions are prone to view both correct predictions and incorrect predictions to be trustworthy. We propose a novel steep slope loss to separate the features w.r.t. correct predictions from the ones w.r.t. incorrect predictions by two slide-like curves that oppose each other.
  arXiv  Detail & Related papers  (2021-09-30T19:19:09Z)
• Private Prediction Sets [72.75711776601973]
  Machine learning systems need reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. We evaluate the method on large-scale computer vision datasets.
  arXiv  Detail & Related papers  (2021-02-11T18:59:11Z)
• Bayes DistNet -- A Robust Neural Network for Algorithm Runtime Distribution Predictions [1.8275108630751844]
  Randomized algorithms are used in many state-of-the-art solvers for constraint satisfaction problems (CSP) and Boolean satisfiability (SAT) problems. Previous state-of-the-art methods directly try to predict a fixed parametric distribution that the input instance follows. This new model achieves robust predictive performance in the low observation setting, as well as handling censored observations.
  arXiv  Detail & Related papers  (2020-12-14T01:15:39Z)

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.