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Hongyang Du

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4 papers

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IS Journal 2026 Journal Article

A Survey on Continuous Unlearning in Generative AI: Approaches and Tradeoffs

Yang Zhao
Hongyang Du
Yijing Lin
Keyi Xiang
Dusit Niyato
H. Vincent Poor

Generative artificial intelligence (GenAI) models have innovated content creation but raise concerns about privacy, security, and regulatory compliance such as General Data Protection Regulation. In response, unlearning techniques have emerged to selectively remove data while preserving the utility of the model. This article reviews unlearning methods in centralized and decentralized settings. These strategies mitigate risks, such as data leakage, membership inference, and bias amplification. By integrating unlearning with continuous or lifelong learning paradigms, GenAI models can adapt dynamically while honoring the “right to be forgotten. ” In existing unlearning methods, we explore key tradeoffs involving computational overhead, accuracy retention, generative quality, and thorough data deletion. Our review covers technical and ethical considerations and future directions, highlighting a balanced path toward responsible GenAI systems.

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AAAI Conference 2025 Conference Paper

Supervised Score-Based Modeling by Gradient Boosting

Changyuan Zhao
Hongyang Du
Guangyuan Liu
Dusit Niyato

Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with the gradient boosting algorithm, a multi-step supervised learning algorithm, to solve supervised learning tasks. However, existing generative model algorithms are often limited by the stochastic nature of the models and the long inference time, impacting prediction performances. Therefore, we propose a Supervised Score-based Model (SSM), which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Via the ablation experiment in selected examples, we demonstrate the outstanding performances of the proposed techniques. Additionally, we compare our model with other probabilistic models, including Natural Gradient Boosting (NGboost), Classification and Regression Diffusion Models (CARD), Diffusion Boosted Trees (DBT), and non-probabilistic gradient boosting models. The experimental results show that our model outperforms existing models in both accuracy and inference time.

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NeurIPS Conference 2025 Conference Paper

VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding

Zongxia Li
Xiyang Wu
Guangyao Shi
Yubin Qin
Hongyang Du
Tianyi Zhou
Dinesh Manocha
Jordan Boyd-Graber

Vision Language models (VLMs) have achieved remarkable success in video understanding tasks. Yet, a key question remains: Do they comprehend visual information or merely learn superficial mappings between visual and textual patterns? Understanding visual cues, particularly those related to physics and common sense, is crucial for AI systems interacting with the physical world. However, existing VLM evaluations primarily rely on positive-control tests using real-world videos that resemble training distributions. While VLMs perform well on such benchmarks, it is unclear whether they grasp underlying visual and contextual signals or simply exploit visual-language correlations. To fill this gap, we propose incorporating negative-control tests, i. e. , videos depicting physically impossible or logically inconsistent scenarios, and evaluating whether models can recognize these violations. True visual understanding should evince comparable performance across both positive and negative tests. Since such content is rare in the real world, we introduce VideoHallu, a synthetic video dataset featuring physics- and commonsense-violating scenes generated using state-of-the-art tools such as Veo2, Sora, and Kling. The dataset includes expert-annotated question-answer pairs spanning four categories of physical and commonsense violations, designed to be straightforward for human reasoning. We evaluate several leading VLMs, including Qwen-2. 5-VL, Video-R1, and VideoChat-R1. Despite their strong performance on real-world benchmarks (e. g. , MVBench, MMVU), these models hallucinate or fail to detect physical or logical violations, revealing fundamental weaknesses in visual understanding. Finally, we explore reinforcement learning-based post-training on our negative dataset: fine-tuning improves performance on VideoHallu without degrading results on standard benchmarks, indicating enhanced visual reasoning in VLMs. Our data is available at https: //github. com/zli12321/VideoHallu. git.

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IJCAI Conference 2024 Conference Paper

Scalable Federated Unlearning via Isolated and Coded Sharding

Yijing Lin
Zhipeng Gao
Hongyang Du
Dusit Niyato
Gui Gui
Shuguang Cui
Jinke Ren

Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the federated unlearning process often introduces extensive storage overhead and consumes substantial computational resources, thus hindering its implementation in practice. To address this issue, this paper proposes a scalable federated unlearning framework based on isolated sharding and coded computing. We first divide distributed clients into multiple isolated shards across stages to reduce the number of clients being affected. Then, to reduce the storage overhead of the central server, we develop a coded computing mechanism by compressing the model parameters across different shards. In addition, we provide the theoretical analysis of time efficiency and storage effectiveness for the isolated and coded sharding. Finally, extensive experiments on two typical learning tasks, i. e. , classification and generation, demonstrate that our proposed framework can achieve better performance than three state-of-the-art frameworks in terms of accuracy, retraining time, storage overhead, and F1 scores for resisting membership inference attacks.

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