35 citations · 41 across the 22 of their papers we have counts for
11 papers · 1 filter
Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation
Xiaofeng Cao, Mingwei Xu, Xin Yu +8
Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI); however, the costs associated with data annotation and model training remain…
An Agentic Framework with LLMs for Solving Complex Vehicle Routing Problems
Ni Zhang, Zhiguang Cao, Jianan Zhou +2
Complex vehicle routing problems (VRPs) remain a fundamental challenge, demanding substantial expert effort for intent interpretation and algorithm design. While large language mod…
Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generation
Kaidong Feng, Zhu Sun, Hui Fang +3
Large Language Models (LLMs) have shown potential in automatic bundle generation but suffer from prohibitive computational costs. Although knowledge distillation offers a pathway t…
Lifelong Learner: Discovering Versatile Neural Solvers for Vehicle Routing Problems
Shaodi Feng, Zhuoyi Lin, Jianan Zhou +5
Deep learning has been extensively explored to solve vehicle routing problems (VRPs), which yields a range of data-driven neural solvers with promising outcomes. However, most neur…
Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles
Shengcai Liu, Hui Ou-yang, Zhiyuan Wang +4
Learning the structure of Bayesian networks (BNs) from data is challenging, especially for datasets involving a large number of variables. The recently proposed divide-and-conquer…
Context-Adaptive Graph Neural Networks for Next POI Recommendation
Yu Lei, Limin Shen, Zhu Sun +2
Next Point-of-Interest (POI) recommendation is a critical task in location-based services, aiming to predict users' next visits based on their check-in histories. While many existi…
Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
Ning Lu, Shengcai Liu, Jiahao Wu +5
Large language models (LLMs) have shown great potential as general-purpose AI assistants across various domains. To fully leverage this potential in specific applications, many com…
BARREL: Boundary-Aware Reasoning for Factual and Reliable LRMs
Junxiao Yang, Jinzhe Tu, Haoran Liu +9
Recent advances in Large Reasoning Models (LRMs) have shown impressive capabilities in mathematical and logical reasoning. However, current LRMs rarely admit ignorance or respond w…
Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization
Hongxu Wang, Zhu Sun, Yingpeng Du +3
Recommender systems (RSs) play a crucial role in shaping our digital interactions, influencing how we access and engage with information across various domains. Traditional researc…
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation
Yingpeng Du, Zhu Sun, Ziyan Wang +3
Large language models (LLMs) provide a promising way for accurate session-based recommendation (SBR), but they demand substantial computational time and memory. Knowledge distillat…
Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization
Hongliang Qiao, Shanshan Feng, Min Zhou +5
The Influence Maximization (IM) problem aims to find a small set of influential users to maximize their influence spread in a social network. Traditional methods rely on fixed diff…