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20222026
most citedOptimal Transport Model Distributional Robustness

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1

With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…

cs.CV2026

Adaptive Subspace Projection for Generative Personalization

Van-Anh Nguyen, Anh Tuan Bui, Tamas Abraham +5

Generative personalization often suffers from the semantic collapsing problem (SCP), where a learned personalized concept overpowers the rest of the text prompt, causing the model…

cs.LG2026

Antibody: Strengthening Defense Against Harmful Fine-Tuning for Large Language Models via Attenuating Harmful Gradient Influence

Quoc Minh Nguyen, Trung Le, Jing Wu +2

Fine-tuning-as-a-service introduces a threat to Large Language Models' safety when service providers fine-tune their models on poisoned user-submitted datasets, a process known as…

cs.CV2024

Connective Viewpoints of Signal-to-Noise Diffusion Models

Khanh Doan, Long Tung Vuong, Tuan Nguyen +5

Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio generation, and complex data interpola…

cs.LG2023★ 1 cited

Optimal Transport Model Distributional Robustness

Van-Anh Nguyen, Trung Le, Anh Tuan Bui +2

Distributional robustness is a promising framework for training deep learning models that are less vulnerable to adversarial examples and data distribution shifts. Previous works h…

cs.CV2022

Multiple Perturbation Attack: Attack Pixelwise Under Different -norms For Better Adversarial Performance

Ngoc N. Tran, Anh Tuan Bui, Dinh Phung +1

Adversarial machine learning has been both a major concern and a hot topic recently, especially with the ubiquitous use of deep neural networks in the current landscape. Adversaria…