no code implementations • 22 Mar 2024 • Zhenrui Yue, Huimin Zeng, Yimeng Lu, Lanyu Shang, Yang Zhang, Dong Wang
The proliferation of online misinformation has posed significant threats to public interest.
1 code implementation • 7 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.
1 code implementation • 25 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.
1 code implementation • 3 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.
1 code implementation • 27 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.
1 code implementation • 22 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.
1 code implementation • 19 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.
no code implementations • 6 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.
no code implementations • 3 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.
1 code implementation • COLING 2022 • Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
In this work, we investigate the potential benefits of question classification for QA domain adaptation.
2 code implementations • 20 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.
1 code implementation • 19 Jul 2022 • Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
Additionally, we design an adversarial training method tailored for sequential recommender systems.
no code implementations • 29 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.
1 code implementation • 1 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.
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.