1 code implementation • 1 Dec 2019 • Lin Gong, Lu Lin, Weihao Song, Hongning Wang
Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data.
no code implementations • 21 Jan 2021 • Xiaoyu Ma, Lu Lin, Yujie Gai
The paper presents a general framework for online updating variable selection and parameter estimation in generalized linear models with streaming datasets.
Variable Selection Methodology
no code implementations • 26 Oct 2021 • Nan Wang, Lu Lin, Jundong Li, Hongning Wang
In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes.
no code implementations • 31 Oct 2021 • Lu Lin, Ethan Blaser, Hongning Wang
The exploitation of graph structures is the key to effectively learning representations of nodes that preserve useful information in graphs.
no code implementations • 1 Nov 2021 • Yujia Wang, Lu Lin, Jinghui Chen
We prove that the proposed communication-efficient distributed adaptive gradient method converges to the first-order stationary point with the same iteration complexity as uncompressed vanilla AMSGrad in the stochastic nonconvex optimization setting.
1 code implementation • 1 Nov 2021 • Lu Lin, Ethan Blaser, Hongning Wang
Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their encouraging performance on graph learning tasks, but they are also shown vulnerability to adversarial attacks.
1 code implementation • 5 May 2022 • Yujia Wang, Lu Lin, Jinghui Chen
We show that in the nonconvex stochastic optimization setting, our proposed FedCAMS achieves the same convergence rate of $O(\frac{1}{\sqrt{TKm}})$ as its non-compressed counterparts.
no code implementations • 10 Jun 2022 • Lu Lin, Weiyu Li
A basic condition for efficient transfer learning is the similarity between a target model and source models.
no code implementations • 29 Sep 2022 • Songtao Liu, Rex Ying, Hanze Dong, Lu Lin, Jinghui Chen, Dinghao Wu
However, the analysis of implicit denoising effect in graph neural networks remains open.
1 code implementation • 30 Sep 2022 • Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, Dinghao Wu
Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.
1 code implementation • 2 Oct 2022 • Lu Lin, Jinghui Chen, Hongning Wang
Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination.
no code implementations • 18 Jun 2023 • Yi Nian, Yurui Chang, Wei Jin, Lu Lin
Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph patterns.
1 code implementation • 18 Sep 2023 • Bochuan Cao, Yuanpu Cao, Lu Lin, Jinghui Chen
In this work, we introduce a Robustly Aligned LLM (RA-LLM) to defend against potential alignment-breaking attacks.
no code implementations • 2 Oct 2023 • Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu
A natural question is "could alignment really prevent those open-sourced large language models from being misused to generate undesired content?''.
no code implementations • 23 Jan 2024 • Chuanbo Liu, Yu Fu, Lu Lin, Elliot L. Elson, Jin Wang
This approach, when combined with the analytical capabilities of a sophisticated deep neural network, enables the accurate estimation of rate constants from observational data in a broad range of biochemical reaction networks.