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.
Learning-Augmented and Randomized Algorithms for Line Aggregation with Delays
arXiv:2607. 27807v1 Announce Type: new Abstract: This paper studies learning-augmented and randomized online aggregation with delays on a line metric.
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arXiv Machine Learning
5d ago
Online Correlation Clustering: Simultaneously Optimizing All $\ell_p$-norms
arXiv:2510. 15076v2 Announce Type: replace Abstract: The $\ell_p$-norm objectives for correlation clustering present a fundamental trade-off between minimizing total disagreements (the $\ell_1$-norm) and ensuring fairness to individual nodes (the $\ell_\infty$-norm).
arXiv Machine Learning
Jun 11
Capacity-Constrained Online Convex Optimization with Delayed Feedback
arXiv:2606. 11711v1 Announce Type: new Abstract: Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives.
arXiv Machine Learning
Jun 2
Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
arXiv:2605. 01752v4 Announce Type: replace Abstract: We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adversarial corruption.
arXiv Machine Learning
Jun 2
Online Packet Scheduling with Deadlines and Learning
arXiv:2606. 00835v1 Announce Type: new Abstract: Network routers that enforce Quality-of-Service (QoS) guarantees must decide, at every clock cycle, which expiring packet of information to transmit, even when the value of the packet is unknown until it is processed.
arXiv AI
Jul 14
Efficient Online Proportional Sampling with Applications to Smoothed Online Learning
arXiv:2607. 10963v1 Announce Type: cross Abstract: We study the problem of efficient online proportional sampling from a high-dimensional domain under a $\sigma$-smoothed adversary, where the sampling distribution is induced by a dynamically evolving weight function defined over a sequence of piecewise-structured partitions.
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
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
arXiv:2605. 28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks.
arXiv Machine Learning
Jul 24
Robust Asynchronous Q-Learning under Reward and State Corruption via Batching
arXiv:2607. 20822v1 Announce Type: new Abstract: Motivated by reinforcement learning in harsh environments, we consider the problem of learning an optimal policy subject to adversarially corrupted feedback.
arXiv Machine Learning
Jun 19
Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem
arXiv:2603. 10184v2 Announce Type: replace-cross Abstract: Statistical inference with bandit data presents fundamental challenges owing to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theory.
arXiv Machine Learning
Jun 17
On Randomized Algorithms in Online Strategic Classification
arXiv:2602. 06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions.
arXiv Machine Learning
Jun 3
Online Learning with Gradient-Variation Interval Regret
arXiv:2606. 03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval.