The paper addresses an online resource allocation problem where a budget must be distributed among multiple arms under censored feedback. Each arm yields a reward only if it is activated by an unknown Bernoulli process and the allocated budget exceeds a random threshold from an unknown parametric distribution. The authors establish a worst‑case regret lower bound of Σ(√T), propose the RA‑UCB algorithm achieving near‑optimal regret when the budget is known, and introduce MG‑UCB for unknown, round‑dependent budgets, validating both approaches experimentally.
By Giovanni Montanari, C\^ome Fiegel, Corentin Pla, Aadirupa Saha, Vianney Perchet
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.
By Gianmarco Genalti, Achraf Azize, Vianney Perchet
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.
By Alexander Ryabchenko, Idan Attias, Daniel M. Roy
arXiv:2607. 23765v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance across multiple domains, but using the most capable model for every query is prohibitive at scale.
By Yifei Li, Zihui Gao, Laks V. S. Lakshmanan
arXiv:2610.32740v1 Announce Type: cross
Abstract: We study strongly adaptive regret for Online Convex Optimization (OCO) with time-varying movement costs, a setting that evaluates a learner's perform...
By Andrew Jacobsen, Emmanuel Esposito, Hao Qiu, Mengxiao Zhang
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.
By Amirmahdi Mirfakhar, Maria-Florina Balcan, Hedyeh Beyhaghi
arXiv:2603. 28201v3 Announce Type: replace Abstract: We revisit the standard perturbation-based approach of Abernethy et al.
By Andrew Jacobsen, Dorian Baudry, Shinji Ito, Nicol\`o Cesa-Bianchi
arXiv:2609.13547v1 Announce Type: new
Abstract: We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most $S$ times. When $S$...
By Mengxiao Zhang
arXiv:2602. 06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions.
By Chase Hutton, Adam Melrod, Han Shao
arXiv:2610.09691v1 Announce Type: cross
Abstract: We study online learning in market making, where, at each round, a market maker posts bid and ask prices before observing the market price and the pr...
By Maria Elena Vischi, Francesco Emanuele Stradi, Alberto Marchesi
arXiv:2609.05895v1 Announce Type: new
Abstract: We study a finite-horizon online resource allocation problem with initial resource capacities proportional to the horizon. In each period, a request ty...
By Menglong Li, Jiawei Zhang
arXiv:2610.07615v1 Announce Type: new
Abstract: Active Feature Acquisition (AFA) is a classification problem in which an agent decides which costly features to acquire before predicting each sample's...
By AbdAlRahman Odeh, Teng-Hui Huang, Hesham El Gamal