The paper investigates online fair allocation of sequential items to agents with heterogeneous preferences, aiming to maximize generalized-mean welfare. In an i.i.d. arrival setting, a pure greedy algorithm achieves near-optimal “~O(1/T)” average regret without needing distributional knowledge. For nonstationary arrivals, the authors show that a single historical sample per distribution suffices to recover the same regret rate, using re-solving algorithms that remain robust to distribution shifts.
By Zongjun Yang, Rachitesh Kumar, Christian Kroer
arXiv:2607. 23310v1 Announce Type: cross Abstract: We study an online variant of discrete fair division under generalized assignment budget constraints.
By Saar Cohen, Nicholas Teh, Paul W. Goldberg, Michael J. Wooldridge
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...
By Qingwen Zhang, Wenjia Wang
arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.
By Maheed H. Ahmed, Mahsa Ghasemi
arXiv:2601. 07144v3 Announce Type: replace-cross Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions.
By Linus Bleistein, Mathieu Dagr\'eou, Francisco Andrade, Thomas Boudou, Aur\'elien Bellet
arXiv:2607. 23772v1 Announce Type: cross Abstract: We study a restless multi-armed bandit (RMAB) problem for a stochastic deadline scheduling application.
By Shakti Sharma, Rahul Meshram
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.
By Christian Coester, Alexa Tudose, Alexander Turoczy
arXiv:2607. 26485v1 Announce Type: cross Abstract: Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly formulated as an optimization problem with diversity constraints to ensure group fairness.
By Keke Huang, Yik Yu Ng, Laks V. S. Lakshmanan, Xiaokui Xiao
arXiv:2609.19963v2 Announce Type: cross
Abstract: Exploration in centralized serial-dictatorship matching bandits must use complete matchings, so learning one player-arm pair can impose regret on oth...
By Lishang Xu, Guodong Ma, Pengcheng Weng, Zixuan Xia
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.
By Fredy Pokou (MRE, CRIStAL)
arXiv:2608. 04669v1 Announce Type: new Abstract: Many social services assign scarce resources, such as housing assistance or hospital interventions, to people who arrive one at a time: each arrival must receive a decision immediately, and the long-run usage of every resource must stay within its capacity.
By Mohammadsaeed Haghi, Mahdi Salmani, Nima Kelidari
arXiv:2606. 10472v1 Announce Type: cross Abstract: Dynamic multi-resource allocation is a central problem in shared computing environments, where users' demands arrive sequentially and resources must be distributed fairly without knowledge of future demands.
By Kaiqi Jiang, Karim El Husseini, Wenzhe Fan, Xinhua Zhang