5 papers · 1 filter
Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attacks
Yaning Jia, Shenyang Deng, Yaoqing Yang +3
Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications, yet they remain highly vulnerable to adversarial attacks that maliciously perturb graph s…
What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning
Yaning Jia, Chunhui Zhang, Xingjian Diao +4
Curriculum learning (CL) - ordering training data from easy to hard - has become a popular strategy for improving reasoning in large language models (LLMs). Yet prior work employs…
KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexity
Yaning Jia, Shenyang Deng, Chiyu Ma +2
Graph Neural Networks (GNNs) have achieved impressive success across a wide range of graph-based tasks, yet they remain highly vulnerable to small, imperceptible perturbations and…
Scaled Supervision is an Implicit Lipschitz Regularizer
Zhongyu Ouyang, Chunhui Zhang, Yaning Jia +1
In modern social media, recommender systems (RecSys) rely on the click-through rate (CTR) as the standard metric to evaluate user engagement. CTR prediction is traditionally framed…
Promoting Fairness in GNNs: A Characterization of Stability
Yaning Jia, Chunhui Zhang
The Lipschitz bound, a technique from robust statistics, can limit the maximum changes in the output concerning the input, taking into account associated irrelevant biased factors.…