2 citations · 5 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
Rising Multi-Armed Bandits with Known Horizons
Seockbean Song, Chenyu Gan, Youngsik Yoon +3
Rising Multi-Armed Bandits (RMABs) model sequential decision problems where each arm's expected reward improves with repeated pulls. In such problems, the value of investing in an…
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.…
On the Sublinear Regret of Continuous K-Max Bandits
Yu Chen, Siwei Wang, Longbo Huang +1
The -Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward is determined by the maximum ou…
Offline Learning for Combinatorial Multi-armed Bandits
Xutong Liu, Xiangxiang Dai, Jinhang Zuo +4
The combinatorial multi-armed bandit (CMAB) is a fundamental sequential decision-making framework, extensively studied over the past decade. However, existing work primarily focuse…
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…
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
Xutong Liu, Siwei Wang, Jinhang Zuo +7
We introduce a novel framework of combinatorial multi-armed bandits (CMAB) with multivariant and probabilistically triggering arms (CMAB-MT), where the outcome of each arm is a …
Can Graph Learning Improve Planning in LLM-based Agents?
Xixi Wu, Yifei Shen, Caihua Shan +8
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests i…
ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models
Siwei Wang, Yifei Shen, Shi Feng +3
Planning is a crucial element of both human intelligence and contemporary large language models (LLMs). In this paper, we initiate a theoretical investigation into the emergence of…