From the 1 of 14 linked papers with an AI index.
1 citations · 1 across the 8 of their papers we have counts for
10 papers · 1 filter
On the Sublinear Regret of Continuous K-Max Bandits
Yu Chen, Siwei Wang, Longbo Huang +1
The paper studies continuous K‑max combinatorial multi‑armed bandits, proposing the DCK‑UCB algorithm with adaptive discretization and bias‑corrected confidence bounds that achieve…
When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models
Youngsik Yoon, Siwei Wang, Wei Chen +1
Mixture-of-Experts (MoE) language models route each token to a small subset of experts, but whether the routes selected by a trained top- router are good ones is rarely evaluate…
Continuous Semantic Caching for Low-Cost LLM Serving
Baran Atalar, Xutong Liu, Jinhang Zuo +3
As Large Language Models (LLMs) become increasingly popular, caching responses so that they can be reused by users with semantically similar queries has become a vital strategy for…
Combinatorial Rising Bandits
Seockbean Song, Youngsik Yoon, Siwei Wang +2
Combinatorial online learning is a fundamental task for selecting the optimal action (or super arm) as a combination of base arms in sequential interactions with systems providing…
Rising Multi-Armed Bandits with Known Horizons
Seockbean Song, Chenyu Gan, Youngsik Yoon +3
The Rising Multi-Armed Bandit (RMAB) framework models environments where expected rewards of arms increase with plays, which models practical scenarios where performance of each op…
Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptation
Xutong Liu, Baran Atalar, Xiangxiang Dai +5
Large Language Models (LLMs) are revolutionizing how users interact with information systems, yet their high inference cost poses serious scalability and sustainability challenges.…