Search Results for author: Lu Lin

Found 18 papers, 7 papers with code

Graph Adversarial Diffusion Convolution

1 code implementation4 Jun 2024 Songtao Liu, Jinghui Chen, Tianfan Fu, Lu Lin, Marinka Zitnik, Dinghao Wu

This paper introduces a min-max optimization formulation for the Graph Signal Denoising (GSD) problem.


XPrompt:Explaining Large Language Model's Generation via Joint Prompt Attribution

no code implementations30 May 2024 Yurui Chang, Bochuan Cao, Yujia Wang, Jinghui Chen, Lu Lin

In this study, we introduce a counterfactual explanation framework based on joint prompt attribution, XPrompt, which aims to explain how a few prompt texts collaboratively influences the LLM's complete generation.

Combinatorial Optimization counterfactual +2

WordGame: Efficient & Effective LLM Jailbreak via Simultaneous Obfuscation in Query and Response

no code implementations22 May 2024 Tianrong Zhang, Bochuan Cao, Yuanpu Cao, Lu Lin, Prasenjit Mitra, Jinghui Chen

The recent breakthrough in large language models (LLMs) such as ChatGPT has revolutionized production processes at an unprecedented pace.

Understanding Cellular Noise with Optical Perturbation and Deep Learning

no code implementations23 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.

On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?

no code implementations2 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?''.

Text Generation

Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM

1 code implementation18 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.

Globally Interpretable Graph Learning via Distribution Matching

no code implementations18 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.

Graph Classification Graph Learning

Spectral Augmentation for Self-Supervised Learning on Graphs

1 code implementation2 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 In-Context Learning for Retrosynthetic Planning

1 code implementation30 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.

Drug Discovery In-Context Learning +3

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

2 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

Graph Structural Attack by Perturbing Spectral Distance

1 code implementation1 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

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 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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