arXiv Machine Learning
Learning Adaptive SED for heterogeneous load balancing
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arXiv Machine Learning
Jun 17
Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach
arXiv:2510. 19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.
arXiv Machine Learning
Jun 30
Learning to Route and Schedule LLMs from User Retrials via Contextual Queueing Bandits
arXiv:2602. 02061v2 Announce Type: replace Abstract: Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling (query prioritization) mechanisms.
arXiv Machine Learning
Oct 15
Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources
The paper introduces Lag, a mean‑field optimal index policy for online assignment problems with reusable resources, such as peer review, call routing, ridesharing, emergency dispatch, and crowdsourcing. By modeling the problem as a contextual restless multi‑armed bandit and deriving an occupancy‑measure linear program, the authors obtain Lag from the dual and prove it is asymptotically optimal in a scaling regime where server copies and arrivals grow together. Experiments on peer‑review and call‑center data show Lag achieves 83–99% of the LP upper bound and outperforms greedy assignment by up to 36% in synthetic settings.
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 AI
Jul 28
WISERouter: LLM Routing with Workload Budget Constraint
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.
arXiv Machine Learning
Sep 16
Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
Meta-LinEXP3 is an online-within-online algorithm designed for adversarial linear contextual bandits with random action sets. It builds a task-level prior from completed tasks to guide an inner LinEXP3 learner, achieving an σO(√n) per‑task regret when context distributions are known and an σO(n^{2/3}) regret with a past‑only regularized moment estimator when they are unknown. The paper also links prior accuracy to transfer regret, showing that better priors yield sublinear, transfer‑dependent regret across tasks, and demonstrates the method on structured hyperspectral tensor sampling.