24 citations · 24 across the 6 of their papers we have counts for
7 papers
GEAR: Generative Expansion and Real Anchoring for Two-Stage Distillation of Tabular Foundation Models
Qi Qin, Jiajie Zhu, Dali Chen +6
Tabular foundation models (TFMs) achieve strong performance through in-context learning, but context-dependent inference imposes substantial latency and memory costs, hindering lar…
Cross-Domain Fake News Detection on Unseen Domains via LLM-Based Domain-Aware User Modeling
Xuankai Yang, Yan Wang, Jiajie Zhu +4
Cross-domain fake news detection (CD-FND) transfers knowledge from a source domain to a target domain and is crucial for real-world fake news mitigation. This task becomes particul…
Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation
Hongyang Liu, Zhu Sun, Tianjun Wei +3
Recent advances in large language models (LLMs) have enabled realistic user simulators for developing and evaluating recommender systems (RSs). However, existing LLM-based simulato…
Adaptive Graph Unlearning
Pengfei Ding, Yan Wang, Guanfeng Liu +1
Graph unlearning, which deletes graph elements such as nodes and edges from trained graph neural networks (GNNs), is crucial for real-world applications where graph data may contai…
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation
Jiajie Zhu, Yan Wang, Feng Zhu +3
Cross-Domain Recommendation (CDR) aims to leverage knowledge from a relatively data-richer source domain to address the data sparsity problem in a relatively data-sparser target do…
Causal Deconfounding via Confounder Disentanglement for Dual-Target Cross-Domain Recommendation
Jiajie Zhu, Yan Wang, Feng Zhu +1
In recent years, dual-target Cross-Domain Recommendation (CDR) has been proposed to capture comprehensive user preferences in order to ultimately enhance the recommendation accurac…