6 citations · 29 across the 24 of their papers we have counts for
20 papers · 1 filter
HiLoRA: Adaptive Hierarchical LoRA Routing for Training-Free Domain Generalization
Ziyi Han, Huanyu Wang, Zeyu Zhang +3
Low-Rank Adaptation (LoRA) has emerged as a widely used technique for adapting large language models (LLMs) to new domains, due to its modular design and broad availability on plat…
Faster, Smaller, and Smarter: Task-Aware Expert Merging for Online MoE Inference
Ziyi Han, Xutong Liu, Ruiting Zhou +2
Sparse Mixture of Experts (SMoE) has become a preferred architecture for scaling Transformer capacity without increasing computational cost, as it activates only a small subset of…
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.…
Online Multi-LLM Selection via Contextual Bandits under Unstructured Context Evolution
Manhin Poon, XiangXiang Dai, Xutong Liu +3
Large language models (LLMs) exhibit diverse response behaviors, costs, and strengths, making it challenging to select the most suitable LLM for a given user query. We study the pr…
A Unified Online-Offline Framework for Co-Branding Campaign Recommendations
Xiangxiang Dai, Xiaowei Sun, Jinhang Zuo +2
Co-branding has become a vital strategy for businesses aiming to expand market reach within recommendation systems. However, identifying effective cross-industry partnerships remai…
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 Logistic Bandits
Xutong Liu, Xiangxiang Dai, Xuchuang Wang +2
We introduce a novel framework called combinatorial logistic bandits (CLogB), where in each round, a subset of base arms (called the super arm) is selected, with the outcome of eac…
Stochastic Bandits Robust to Adversarial Attacks
Xuchuang Wang, Jinhang Zuo, Xutong Liu +2
This paper investigates stochastic multi-armed bandit algorithms that are robust to adversarial attacks, where an attacker can first observe the learner's action and {then} alter t…
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 …
Cost-Effective Online Multi-LLM Selection with Versatile Reward Models
Xiangxiang Dai, Jin Li, Xutong Liu +2
With the rapid advancement of large language models (LLMs), the diversity of multi-LLM tasks and the variability in their pricing structures have become increasingly important, as…
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
Hantao Yang, Xutong Liu, Zhiyong Wang +4
We study the problem of federated contextual combinatorial cascading bandits, where agents collaborate under the coordination of a central server to provide tailore…
Online Clustering of Bandits with Misspecified User Models
Zhiyong Wang, Jize Xie, Xutong Liu +2
The contextual linear bandit is an important online learning problem where given arm features, a learning agent selects an arm at each round to maximize the cumulative rewards in t…
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +4
We study contextual combinatorial bandits with probabilistically triggered arms (CMAB-T) under a variety of smoothness conditions that capture a wide range of applications, suc…
Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits
Zhiyong Wang, Xutong Liu, Shuai Li +1
Conversational contextual bandits elicit user preferences by occasionally querying for explicit feedback on key-terms to accelerate learning. However, there are aspects of existing…
On-Demand Communication for Asynchronous Multi-Agent Bandits
Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang +4
This paper studies a cooperative multi-agent multi-armed stochastic bandit problem where agents operate asynchronously -- agent pull times and rates are unknown, irregular, and het…
Federated Online Clustering of Bandits
Xutong Liu, Haoru Zhao, Tong Yu +2
Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems. A line of works, called the clustering of bandits (CLUB), utilize…
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +3
In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the dependency of the batch-size in the regret bound, where is the total number of arms…
Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
Xutong Liu, Jinhang Zuo, Xiaowei Chen +2
Multi-layered network exploration (MuLaNE) problem is an important problem abstracted from many applications. In MuLaNE, there are multiple network layers where each node has an im…
Online Competitive Influence Maximization
Jinhang Zuo, Xutong Liu, Carlee Joe-Wong +2
Online influence maximization has attracted much attention as a way to maximize influence spread through a social network while learning the values of unknown network parameters. M…
Graphlet Count Estimation via Convolutional Neural Networks
Xutong Liu, Yu-Zhen Janice Chen, John C. S. Lui +1
Graphlets are defined as k-node connected induced subgraph patterns. For an undirected graph, 3-node graphlets include close triangle and open triangle. When k = 4, there are six t…