ML Anthology

Cong, Weilin

7 publications

ICLR 2025 Learning Graph Quantized Tokenizers Limei Wang, Kaveh Hassani, Si Zhang, Dongqi Fu, Baichuan Yuan, Weilin Cong, Zhigang Hua, Hao Wu, Ning Yao, Bo Long
LoG 2023 BeMap: Balanced Message Passing for Fair Graph Neural Network Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong
ICLR 2023 Do We Really Need Complicated Model Architectures for Temporal Networks? Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, Mehrdad Mahdavi
AISTATS 2023 Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection Weilin Cong, Mehrdad Mahdavi
ICLR 2022 Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Mahmut Kandemir, Anand Sivasubramaniam
NeurIPS 2021 On Provable Benefits of Depth in Training Graph Convolutional Networks Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
NeurIPS 2020 GCN Meets GPU: Decoupling “When to Sample” from “How to Sample” Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, Mahmut Kandemir