Learning-Augmented Online Allocation under Unreliable Advice: Robustness, Exposure Fairness, and Distribution Shift

• URL: http://arxiv.org/abs/2608.26889v1
• Date: Thu, 27 Aug 2026 09:46:04 GMT
• Title: Learning-Augmented Online Allocation under Unreliable Advice: Robustness, Exposure Fairness, and Distribution Shift
• Abstract summary: Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness.<n>We study an online allocation problem with finite candidate sets, irreversible decisions, and exposure constraints.<n>We propose a robust and fair rule combining advice with a conservative fallback and fairness correction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-augmented algorithms improve online decisions using predictions, but unreliable advice may harm efficiency and fairness. We study an online allocation problem with finite candidate sets, irreversible decisions, and exposure constraints. We propose a robust and fair rule combining advice with a conservative fallback and fairness correction. Under bounded-error assumptions, we prove consistency and robustness with loss proportional to prediction error. Experiments show stability under adversarial advice and significant reductions in exposure disparity.

Related papers

• Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning [63.24624171679711]
  Large language model (LLM)-based agents often make suboptimal tool-use decisions.<n>We propose TRUST, which incorporates uncertainty quantification into reward design as a repulsive force for maintaining uncertainty separation.
  arXiv  Detail & Related papers  (2026-06-05T07:08:34Z)
• Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization [7.4627679994408735]
  Uncertainty quantification is crucial in safety-critical systems, where decisions must be made under uncertainty.<n>Online conformal prediction is a principled online uncertainty quantification method that dynamically constructs a prediction set at each time step.<n>We propose a novel learning method for online conformal prediction with partial feedback from an adaptive adversary.
  arXiv  Detail & Related papers  (2026-04-20T09:07:40Z)
• Learning-Augmented Ski Rental with Discrete Distributions: A Bayesian Approach [4.702729080310267]
  We propose a discrete Bayesian framework that maintains exact posterior distributions over the time horizon.<n>Our algorithm achieves prior-dependent competitive guarantees and gracefully interpolates between worst-case and fully-informed settings.<n>This framework naturally extends to incorporate multiple predictions, non-uniform priors, and contextual information.
  arXiv  Detail & Related papers  (2025-12-08T08:56:25Z)
• Uncertainty-Aware Post-Hoc Calibration: Mitigating Confidently Incorrect Predictions Beyond Calibration Metrics [6.9681910774977815]
  This paper presents a post-hoc calibration framework to enhance calibration quality and uncertainty-aware decision-making.<n>A comprehensive evaluation is conducted using calibration metrics, uncertainty-aware performance measures, and empirical conformal coverage.<n> Experiments show that the proposed method achieves lower confidently incorrect predictions, and competitive Expected Error compared with isotonic and focal-loss baselines.
  arXiv  Detail & Related papers  (2025-10-19T23:55:36Z)
• AdaSwitch: An Adaptive Switching Meta-Algorithm for Learning-Augmented Bounded-Influence Problems [9.387255955861162]
  We study a class of multi-period online decision-making problems with sequence-based predictions.<n>In each period, the decision-maker observes the realized request and must take an irrevocable action that yields a reward or incurs a cost.<n>We introduce a bounded-influence framework, in which past decisions and requests exert only limited impact on the future optimal reward.<n>Within this framework, we propose the AdaSwitch meta-algorithm, which exploits predictions to attain performance close to the offline benchmark when predictions are accurate.
  arXiv  Detail & Related papers  (2025-09-02T13:26:23Z)
• Knowing When to Stop Matters: A Unified Algorithm for Online Conversion under Horizon Uncertainty [3.6118546479656772]
  A key challenge in online conversion is managing decisions under horizon uncertainty.<n>We propose a unified algorithm that achieves optimal competitive guarantees across these horizon models.<n>We extend the algorithm to a learning-augmented version, leveraging horizon predictions to adaptively balance performance.
  arXiv  Detail & Related papers  (2025-02-06T07:06:06Z)
• Adaptive Conformal Inference by Betting [51.272991377903274]
  We consider the problem of adaptive conformal inference without any assumptions about the data generating process.<n>Existing approaches for adaptive conformal inference are based on optimizing the pinball loss using variants of online gradient descent.<n>We propose a different approach for adaptive conformal inference that leverages parameter-free online convex optimization techniques.
  arXiv  Detail & Related papers  (2024-12-26T18:42:08Z)
• Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching [61.73532883992135]
  We propose a new uncertainty-aware stereo matching framework.<n>We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty.
  arXiv  Detail & Related papers  (2024-12-24T23:28:20Z)
• LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization [56.67706781191521]
  An adversary can introduce outliers by corrupting loss functions in an arbitrary number of k, unknown to the learner. We present a robust online rounds optimization framework, where an adversary can introduce outliers by corrupting loss functions in an arbitrary number of k, unknown.
  arXiv  Detail & Related papers  (2024-08-12T17:08:31Z)
• Binary Classification with Confidence Difference [100.08818204756093]
  This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven.
  arXiv  Detail & Related papers  (2023-10-09T11:44:50Z)
• Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors [91.3755431537592]
  This article develops a framework for learning optimal strategies in real-world matching markets. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. We prove that participants can be better off with multi-stage matching compared to single-stage matching.
  arXiv  Detail & Related papers  (2021-02-13T19:25:52Z)
• Prediction with Corrupted Expert Advice [67.67399390910381]
  We prove that a variant of the classical Multiplicative Weights algorithm with decreasing step sizes achieves constant regret in a benign environment. Our results reveal a surprising disparity between the often comparable Follow the Regularized Leader (FTRL) and Online Mirror Descent (OMD) frameworks.
  arXiv  Detail & Related papers  (2020-02-24T14:39:55Z)

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.