arXiv Machine Learning
Online Budget Allocation with Censored Semi-Bandit Feedback
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arXiv Machine Learning
Oct 22
Learning to Allocate Resources with Censored Feedback
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.
arXiv Machine Learning
Jun 2
Online Packet Scheduling with Deadlines and Learning
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arXiv Machine Learning
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Parameter-Free Heavy-Tailed Bandits
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arXiv Machine Learning
Jun 30
Optimal Regret for Single Index Bandits
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arXiv Machine Learning
Oct 2
Regret Analysis of Retry-Based Bandits
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arXiv Machine Learning
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Meritocratic Fairness via $K$-Shapley Values in Budgeted Combinatorial Bandits with Full-Bandit Feedback
arXiv:2605. 00762v2 Announce Type: replace Abstract: We study meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback, where a learner selects at most $K$ arms per time step and observes only the noisy aggregate reward of the selected set.