Search Results for author: Zhenrui Yue

Found 15 papers, 11 papers with code

Federated Recommendation via Hybrid Retrieval Augmented Generation

1 code implementation7 Mar 2024 Huimin Zeng, Zhenrui Yue, Qian Jiang, Dong Wang

To this end, we propose GPT-FedRec, a federated recommendation framework leveraging ChatGPT and a novel hybrid Retrieval Augmented Generation (RAG) mechanism.

Hallucination Privacy Preserving +2

LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking

1 code implementation25 Oct 2023 Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira, Dong Wang, Even Oldridge

Recently, large language models (LLMs) have exhibited significant progress in language understanding and generation.

Movie Recommendation

Linear Recurrent Units for Sequential Recommendation

1 code implementation3 Oct 2023 Zhenrui Yue, Yueqi Wang, Zhankui He, Huimin Zeng, Julian McAuley, Dong Wang

State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models.

Language Modelling Sequential Recommendation

Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning

1 code implementation27 May 2023 Zhenrui Yue, Huimin Zeng, Mengfei Lan, Heng Ji, Dong Wang

With emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only limited data access is provided for training.

Event Detection Meta-Learning

MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning

1 code implementation22 May 2023 Zhenrui Yue, Huimin Zeng, Yang Zhang, Lanyu Shang, Dong Wang

As such, MetaAdapt can learn how to adapt the misinformation detection model and exploit the source data for improved performance in the target domain.

Meta-Learning Misinformation +1

QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation

1 code implementation19 Oct 2022 Zhenrui Yue, Huimin Zeng, Bernhard Kratzwald, Stefan Feuerriegel, Dong Wang

Unlike existing approaches, we generate pseudo labels and propose to train the model via a novel attention-based contrastive adaptation method.

Contrastive Learning Data Augmentation +2

Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup

no code implementations6 Oct 2022 Huimin Zeng, Zhenrui Yue, Ziyi Kou, Lanyu Shang, Yang Zhang, Dong Wang

Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process.

Contrastive Learning Misinformation +1

On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks

no code implementations3 Oct 2022 Huimin Zeng, Zhenrui Yue, Yang Zhang, Ziyi Kou, Lanyu Shang, Dong Wang

In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems.

Adversarial Attack

Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19

2 code implementations20 Aug 2022 Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang

However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e. g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation.

Domain Adaptation Misinformation

Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation

no code implementations29 Mar 2022 Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang

Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time.

energy management Inductive Bias +2

Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction

1 code implementation1 Sep 2021 Zhenrui Yue, Zhankui He, Huimin Zeng, Julian McAuley

Under this setting, we propose an API-based model extraction method via limited-budget synthetic data generation and knowledge distillation.

Data Poisoning Knowledge Distillation +5

Contrastive Domain Adaptation for Question Answering using Limited Text Corpora

1 code implementation EMNLP 2021 Zhenrui Yue, Bernhard Kratzwald, Stefan Feuerriegel

Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective.

Domain Adaptation Question Answering +2

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