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Chaochao Chen 0001

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6 papers

1 author row

ICLR Conference 2025 Conference Paper

Controllable Unlearning for Image-to-Image Generative Models via ϵ-Constrained Optimization

Xiaohua Feng 0002
Yuyuan Li 0001
Chaochao Chen 0001
Li Zhang
Longfei Li
Jun Zhou 0011
Xiaolin Zheng

While generative models have made significant advancements in recent years, they also raise concerns such as privacy breaches and biases. Machine unlearning has emerged as a viable solution, aiming to remove specific training data, e.g., containing private information and bias, from models. In this paper, we study the machine unlearning problem in Image-to-Image (I2I) generative models. Previous studies mainly treat it as a single objective optimization problem, offering a solitary solution, thereby neglecting the varied user expectations towards the trade-off between complete unlearning and model utility. To address this issue, we propose a controllable unlearning framework that uses a control coefficient $\epsilon$ to control the trade-off. We reformulate the I2I generative model unlearning problem into a $\epsilon$-constrained optimization problem and solve it with a gradient-based method to find optimal solutions for unlearning boundaries. These boundaries define the valid range for the control coefficient. Within this range, every yielded solution is theoretically guaranteed with Pareto optimality. We also analyze the convergence rate of our framework under various control functions. Extensive experiments on two benchmark datasets across three mainstream I2I models demonstrate the effectiveness of our controllable unlearning framework.

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ICML Conference 2025 Conference Paper

Efficient Source-free Unlearning via Energy-Guided Data Synthesis and Discrimination-Aware Multitask Optimization

Xiuyuan Wang 0002
Chaochao Chen 0001
Weiming Liu 0005
Xinting Liao
Fan Wang 0020
Xiaolin Zheng

With growing privacy concerns and the enforcement of data protection regulations, machine unlearning has emerged as a promising approach for removing the influence of forget data while maintaining model performance on retain data. However, most existing unlearning methods require access to the original training data, which is often impractical due to privacy policies, storage constraints, and other limitations. This gives rise to the challenging task of source-free unlearning, where unlearning must be accomplished without accessing the original training data. Few existing source-free unlearning methods rely on knowledge distillation and model retraining, which impose substantial computational costs. In this work, we propose the Data Synthesis-based Discrimination-Aware (DSDA) unlearning framework, which enables efficient source-free unlearning in two stages: (1) Accelerated Energy-Guided Data Synthesis (AEGDS), which employs Langevin dynamics to model the training data distribution while integrating Runge–Kutta methods and momentum to enhance efficiency. (2) Discrimination-Aware Multitask Optimization (DAMO), which refines the feature distribution of retain data and mitigates the gradient conflicts among multiple unlearning objectives. Extensive experiments on three benchmark datasets demonstrate that DSDA outperforms existing unlearning methods, validating its effectiveness and efficiency in source-free unlearning.

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ICML Conference 2025 Conference Paper

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models

Xinting Liao
Weiming Liu 0005
Jiaming Qian
Pengyang Zhou 0001
Jiahe Xu 0003
Wenjie Wang 0007
Chaochao Chen 0001
Xiaolin Zheng

Federated prompt learning (FPL) for vision-language models is a powerful approach to collaboratively adapt models across distributed clients while preserving data privacy. However, existing FPL approaches suffer from a trade-off between performance and robustness, particularly in out-of-distribution (OOD) shifts, limiting their reliability in real-world scenarios. The inherent in-distribution (ID) data heterogeneity among different clients makes it more challenging to maintain this trade-off. To fill this gap, we introduce a Federated OOD-aware Context Optimization (FOCoOp) framework, which captures diverse distributions among clients using ID global prompts, local prompts, and OOD prompts. Specifically, FOCoOp leverages three sets of prompts to create both class-level and distribution-level separations, which adapt to OOD shifts through bi-level distributionally robust optimization. Additionally, FOCoOp improves the discrimination consistency among clients, i. e. , calibrating global prompts, seemly OOD prompts, and OOD prompts by Semi-unbalanced optimal transport. The extensive experiments on real-world datasets demonstrate that FOCoOp effectively captures decentralized heterogeneous distributions and enhances robustness of different OOD shifts. The project is available at GitHub.

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ICML Conference 2024 Conference Paper

One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment

Chaochao Chen 0001
Jiaming Zhang 0009
Yuyuan Li 0001
Zhongxuan Han

The abundance of free internet data offers unprecedented opportunities for researchers and developers, but it also poses privacy risks. Utilizing data without explicit consent raises critical challenges in protecting personal information. Unlearnable examples have emerged as a feasible protection approach, which renders the data unlearnable, i. e. , useless to third parties, by injecting imperceptible perturbations. However, these perturbations only exhibit unlearnable effects on either a particular dataset or label-consistent scenarios, thereby lacking broad applicability. To address both issues concurrently, we propose a universal perturbation generator that harnesses data with concept unlearnability, thereby broadening the scope of unlearnability beyond specific datasets or labels. Specifically, we leverage multi-modal pre-trained models to establish a connection between the data concepts in a shared embedding space. This connection enables the information transformation from image data to text concepts. Consequently, we can align the text embedding using concept-wise discriminant loss, and render the data unlearnable. Extensive experiments conducted on real-world datasets demonstrate the concept unlearnability, i. e. , cross-dataset transferability and label-agnostic utility, of our proposed unlearnable examples, as well as their robustness against attacks.

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ICML Conference 2024 Conference Paper

Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation

Weiming Liu 0005
Xiaolin Zheng
Chaochao Chen 0001
Jiahe Xu 0003
Xinting Liao
Fan Wang 0020
Yanchao Tan
Yew-Soon Ong

Cross-Domain Recommendation (CDR) have become increasingly appealing by leveraging useful information to tackle the data sparsity problem across domains. Most of latest CDR models assume that domain-shareable user-item information (e. g. , rating and review on overlapped users or items) are accessible across domains. However, these assumptions become impractical due to the strict data privacy protection policy. In this paper, we propose Reducing Item Discrepancy (RidCDR) model on solving Privacy-Preserving Cross-Domain Recommendation (PPCDR) problem. Specifically, we aim to enhance the model performance on both source and target domains without overlapped users and items while protecting the data privacy. We innovatively propose private-robust embedding alignment module in RidCDR for knowledge sharing across domains while avoiding negative transfer privately. Our empirical study on Amazon and Douban datasets demonstrates that RidCDR significantly outperforms the state-of-the-art models under the PPCDR without overlapped users and items.

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ECAI Conference 2020 Conference Paper

Secure Social Recommendation Based on Secret Sharing

Chaochao Chen 0001
Liang Li
Bingzhe Wu
Cheng Hong 0001
Li Wang 0056
Jun Zhou 0011

Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many commercial platforms (e. g. Amazon) and they are mainly built based on user-item interactions. Besides, social platforms (e. g. Facebook) have rich resources of user social information. It is well known that social information, which is rich on social platforms such as Facebook, are useful to build intelligent recommender systems. It is anticipated to combine the social information with the user-item ratings to improve the overall recommendation performance. Most existing recommendation models are built based on the assumptions that the social information are available. However, different platforms are usually reluctant to (or can not) share their data due to certain concerns. In this paper, we first propose a SEcure SOcial RECommendation (SeSoRec) framework which is able to (1) collaboratively mine knowledge from social platform to improve the recommendation performance of the rating platform, and (2) securely keep the raw data of both platforms. We then propose a Secret Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and prove its correctness and security theoretically. By applying minibatch gradient descent, SeSoRec has linear time complexities in terms of both computation and communication. The comprehensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed SeSoRec and SSMM.

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