8 papers
Learning to Route and Schedule LLMs from User Retrials via Contextual Queueing Bandits
Seoungbin Bae, Junyoung Son, Dabeen Lee
Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling (query prioritization) mechanism…
Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret
Seoungbin Bae, Dabeen Lee
Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates. Under stochastic contexts, existing algor…
Neural Logistic Bandits
Seoungbin Bae, Dabeen Lee
We study the problem of neural logistic bandits, where the main task is to learn an unknown reward function within a logistic link function using a neural network. Existing approac…
Queue Length Regret Bounds for Contextual Queueing Bandits
Seoungbin Bae, Garyeong Kang, Dabeen Lee
We introduce contextual queueing bandits, a new context-aware framework for scheduling while simultaneously learning unknown service rates. Individual jobs carry heterogeneous cont…
Primal-Dual Policy Optimization for Linear CMDPs with Adversarial Losses
Kihyun Yu, Seoungbin Bae, Dabeen Lee
Existing work on linear constrained Markov decision processes (CMDPs) has primarily focused on stochastic settings, where the losses and costs are either fixed or drawn from fixed…
Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs
Hoyeol Yoon, Seoungbin Bae, Nam Ho-Nguyen +1
Physics-informed neural networks (PINNs) are a promising approach for solving partial differential equations (PDEs). Their training, however, is often difficult because multiple lo…