2 citations · 4 across the 13 of their papers we have counts for
17 papers
Beyond Reward Suppression: Near-Optimal Offline Attacks on Warm-Start Bandits with Bounded Rewards
Qirun Zeng, Manhin Poon, Xiangxiang Dai +2
Adversarial attacks on bandits aim to mislead a learner toward a target arm while keeping the attack cost small. Existing attacks typically achieve this by suppressing non-target a…
Quality-Aware Cross-Model Computation Reuse
Jin Cheng, Xiangxiang Dai, Maoli Liu +3
An intermediate result computed by one model can be reused by other models to perform their tasks. Existing work mainly focuses on practical execution, leaving a theoretical gap in…
Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens
Yizhen Yao, Qinglin Zhu, Runcong Zhao +4
Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality. While revocable decoding strategie…
Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routing
Zeyu Zhang, Xiangxiang Dai, Ziyi Han +2
Large language models (LLMs) are typically governed by post-training alignment (e.g., RLHF or DPO), which yields a largely static policy during deployment and inference. However, r…
Trading Vector Data in Vector Databases
Jin Cheng, Xiangxiang Dai, Ningning Ding +2
Vector data trading is essential for cross-domain learning with vector databases, yet it remains largely unexplored. We study this problem under online learning, where sellers face…
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…
Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits
Maoli Liu, Zhuohua Li, Xiangxiang Dai +1
Conversational recommender systems proactively query users with relevant "key terms" and leverage the feedback to elicit users' preferences for personalized recommendations. Conver…
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…
A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses
Xiangxiang Dai, Yuejin Xie, Maoli Liu +4
Prompt-based offline methods are commonly used to optimize large language model (LLM) responses, but evaluating these responses is computationally intensive and often fails to acco…
Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
Zhuohua Li, Maoli Liu, Xiangxiang Dai +1
The contextual multi-armed bandit (MAB) problem is crucial in sequential decision-making. A line of research, known as online clustering of bandits, extends contextual MAB by group…
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…
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
Xiangxiang Dai, Zeyu Zhang, Peng Yang +3
The rapid evolution of multimedia and computer vision technologies requires adaptive visual model deployment strategies to effectively handle diverse tasks and varying environments…
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…