Search Results for author: Yuyuan Li

Found 9 papers, 1 papers with code

Post-Training Attribute Unlearning in Recommender Systems

no code implementations11 Mar 2024 Chaochao Chen, Yizhao Zhang, Yuyuan Li, Dan Meng, Jun Wang, Xiaoli Zheng, Jianwei Yin

The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers.

Attribute Recommendation Systems

Check, Locate, Rectify: A Training-Free Layout Calibration System for Text-to-Image Generation

no code implementations27 Nov 2023 Biao Gong, Siteng Huang, Yutong Feng, Shiwei Zhang, Yuyuan Li, Yu Liu

To align the generated image with layout instructions, we present a training-free layout calibration system SimM that intervenes in the generative process on the fly during inference time.

Text-to-Image Generation

Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems

no code implementations6 Oct 2023 Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Zhongxuan Han, Dan Meng, Jun Wang

To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance.

Attribute Recommendation Systems

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

no code implementations4 Sep 2023 Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Weiming Liu, Jun Wang, Wenjie Cheng, Yuyuan Li

By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously.

Fairness Recommendation Systems

Federated Unlearning via Active Forgetting

no code implementations7 Jul 2023 Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Jiaming Zhang

To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings.

Federated Learning Incremental Learning +1

Selective and Collaborative Influence Function for Efficient Recommendation Unlearning

no code implementations20 Apr 2023 Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Biao Gong, Jun Wang

In this paper, we first identify two main disadvantages of directly applying existing unlearning methods in the context of recommendation, i. e., (i) unsatisfactory efficiency for large-scale recommendation models and (ii) destruction of collaboration across users and items.

Recommendation Systems

VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval

1 code implementation CVPR 2023 Siteng Huang, Biao Gong, Yulin Pan, Jianwen Jiang, Yiliang Lv, Yuyuan Li, Donglin Wang

Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computational burdens with much more parameters, but also leads to the knowledge forgetting from upstream models.

Cross-Modal Retrieval Retrieval +1

Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation

no code implementations22 Mar 2022 Yuyuan Li, Xiaolin Zheng, Chaochao Chen, Junlin Liu

The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items.

Collaborative Filtering Machine Unlearning +1

Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling

no code implementations13 Nov 2019 Mengying Zhu, Xiaolin Zheng, Yan Wang, Yuyuan Li, Qianqiao Liang

Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods.

Decision Making Management +1

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