Search Results for author: Lu Lin

Found 11 papers, 1 papers with code

Spectral Augmentation for Self-Supervised Learning on Graphs

no code implementations2 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.

Contrastive Learning Node Classification +3

FusionRetro: Molecule Representation Fusion via Reaction Graph for Retrosynthetic Planning

no code implementations30 Sep 2022 Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Peilin Zhao, Jian Tang, Rex Ying, Lu Lin, Dinghao Wu

Comprehensive experiments show that by fusing in context information over routes, our model significantly improves the performance of retrosynthetic planning over baselines that are not context-aware, especially for long synthetic routes.

Drug Discovery Multi-step retrosynthesis +1

A Correlation-Ratio Transfer Learning and Variational Stein's Paradox

no code implementations10 Jun 2022 Lu Lin, Weiyu Li

A basic condition for efficient transfer learning is the similarity between a target model and source models.

Transfer Learning

Communication-Efficient Adaptive Federated Learning

no code implementations5 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.

Federated Learning Quantization +1

Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization

no code implementations1 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.

Graph Structural Attack by Perturbing Spectral Distance

no code implementations1 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.

Graph Learning

Graph Embedding with Hierarchical Attentive Membership

no code implementations31 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.

Graph Embedding Link Prediction +1

Unbiased Graph Embedding with Biased Graph Observations

no code implementations26 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.

Fairness Graph Embedding

A General Framework of Online Updating Variable Selection for Generalized Linear Models with Streaming Datasets

no code implementations21 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

JNET: Learning User Representations via Joint Network Embedding and Topic Embedding

1 code implementation1 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.

Link Prediction Network Embedding

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