Sheng Zhou

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

2020 · arXiv (Cornell University)

Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification …

Distilling Holistic Knowledge with Graph Neural Networks

2021 · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to a smaller learnable student network. Existing KD methods have mainly considered two types of knowledge, namely the individual knowledge and the …

Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation

2022 · IEEE Transactions on Knowledge and Data Engineering

Recommender system usually suffers from severepopularity bias— the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such skewed distribution may result from the users’conformityto the group, which deviates from reflecting …

Adaptive Privacy-Preserving Coded Computing With Hierarchical Task Partitioning

2023 · arXiv (Cornell University)

Distributed computing is known as an emerging and efficient technique to support various intelligent services, such as large-scale machine learning. However, privacy leakage and random delays from straggling servers pose significant challenges. To address these …

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective

2025

Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation …