arXiv AI
Jul 28
Online Fair Division with Budget Constraints
arXiv:2607. 23310v1 Announce Type: cross Abstract: We study an online variant of discrete fair division under generalized assignment budget constraints.
Learning-Augmented Online Allocation under Unreliable Advice: Robustness, Exposure Fairness, and Distribution Shift
The paper introduces a learning‑augmented algorithm for online allocation that handles unreliable predictions. It addresses finite candidate sets, irreversible decisions, and exposure constraints by combining advice with a conservative fallback and a fairness correction. The authors prove consistency and robustness under bounded‑error assumptions and demonstrate experimentally that the method remains stable against adversarial advice while substantially reducing exposure disparity.
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arXiv Machine Learning
Aug 12
Risk-Averse Wasserstein Distributionally Robust Online Learning
arXiv:2602. 20403v2 Announce Type: replace Abstract: We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from a Wasserstein ambiguity set centered at past observations.
arXiv Machine Learning
Jul 31
Learning-Augmented Algorithms for Online Vertex Cover
arXiv:2606. 22831v2 Announce Type: replace-cross Abstract: This paper studies learning-augmented online weighted vertex cover with local advice and a tradeoff parameter $\lambda \in (0,1)$.
arXiv Machine Learning
Jun 2
Towards Optimal Robustness in Learning-Augmented Paging
arXiv:2606. 01342v1 Announce Type: cross Abstract: Learning-augmented paging has been extensively studied in recent years.
arXiv Machine Learning
Sep 7
Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications
Learning-augmented algorithms combine fallible predictions with formal performance guarantees. This survey reviews prediction interfaces, error measures, consistency–robustness trade-offs, and five construction mechanisms across online optimization, caching, learned data structures, graph problems, and mechanism design. It distinguishes theorem-level upper bounds from matched asymptotic dependence, separates formal guarantees from empirical evidence, and outlines open problems in cost-aware prediction, endogenous error, semantic predictors, and benchmarking.
arXiv Machine Learning
Jun 5
Learning-Augmented Online Minimization with Dual Predictions
arXiv:2606. 05380v1 Announce Type: cross Abstract: We present learning-augmented algorithms for two general classes of online minimization problems: metrical task systems and laminar set cover.
arXiv Machine Learning
Sep 1
RL-FAT: Reinforcement Learning for Fair Adversarial Training
arXiv:2608.29247v1 Announce Type: new Abstract: Deep neural networks remain highly vulnerable to adversarial perturbations, and adversarial training (AT) has become a widely used approach for improvi...
arXiv Machine Learning
Jun 18
Fair Online Resource Allocation
arXiv:2606. 18679v1 Announce Type: cross Abstract: We study the problem of fair online resource allocation, motivated by applications such as refugee resettlement and airline scheduling, where agents arrive sequentially and must be assigned to facilities with limited capacities.
arXiv Machine Learning
Sep 3
Cantelli Constrained Policy Optimization
The paper introduces Canary, a risk‑averse reinforcement learning method that optimizes Value‑at‑Risk (VaR) constraints. By applying Cantelli’s inequality, Canary derives a tractable, conservative, and smooth bound on the VaR constraint using only the first two moments of the cost return, yielding a stable constraint estimator even with tight violation thresholds. Extending the trust‑region framework of Constrained Policy Optimization (CPO), the authors provide worst‑case bounds for policy improvement and constraint violation, and empirically demonstrate that Canary reliably satisfies the VaR constraint in every tested environment.
arXiv Machine Learning
2d ago
Robust Risk-Sensitive Reinforcement Learning from Corrupted Human Feedback
arXiv:2609.38938v1 Announce Type: new Abstract: Reinforcement learning with human feedback (RLHF) learns from human comparisons, which can be corrupted or deliberately manipulated. This paper studies...
arXiv Machine Learning
Sep 22
Attack-Resistant Uniform Fairness for Linear and Smooth Contextual Bandits
arXiv:2602.04125v2 Announce Type: replace-cross Abstract: Modern digital platforms use contextual bandits to allocate valuable exposure and opportunities among competing participants. Fair treatment...
arXiv Machine Learning
Sep 23
FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks
FairMean is a new approach for distributed learning that addresses the conflict between fairness and robustness to label poisoning attacks. It assigns weights to client gradients based on a bounded, nondecreasing function of local loss, giving higher weight to high‑loss clients to promote fairness while limiting the influence of poisoned clients. The method is shown to improve fairness compared to standard average‑loss minimization and to reduce accuracy variance while boosting worst‑client accuracy in experiments.