Minh N. Vu

According to our database, Minh N. Vu authored at least 28 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Active Gradient Manipulation for Privacy Breaching in Vertical Federated Learning.

Tre' R. Jeter, Minh N. Vu, Raed Alharbi, Jung-Taek Seo, My T. Thai

IEEE Trans. Artif. Intell., October, 2025

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling.

Minh N. Vu, Brian K. Tran, Syed A. Shah, Geigh Zollicoffer, Nhat Hoang-Xuan, Manish Bhattarai

CoRR, September, 2025

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models.

Quan Nguyen, Minh N. Vu, Truc Nguyen, My T. Thai

CoRR, June, 2025

Diverging Towards Hallucination: Detection of Failures in Vision-Language Models via Multi-token Aggregation.

Geigh Zollicoffer, Minh N. Vu, Manish Bhattarai

CoRR, May, 2025

TT-LoRA MoE: Unifying Parameter-Efficient Fine-Tuning and Sparse Mixture-of-Experts.

Pradip Kunwar, Minh N. Vu, Maanak Gupta, Mahmoud Abdelsalam, Manish Bhattarai

CoRR, April, 2025

Topological Signatures of Adversaries in Multimodal Alignments.

Minh N. Vu, Geigh Zollicoffer, Huy Mai, Ben Nebgen, Boian S. Alexandrov, Manish Bhattarai

CoRR, January, 2025

LoRID: Low-Rank Iterative Diffusion for Adversarial Purification.

Geigh Zollicoffer, Minh N. Vu, Ben Nebgen, Juan Castorena, Boian S. Alexandrov, Manish Bhattarai

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

LaFA: Latent Feature Attacks on Non-negative Matrix Factorization.

Minh N. Vu, Ben Nebgen, Erik Skau, Geigh Zollicoffer, Juan Castorena, Kim Ø. Rasmussen, Boian S. Alexandrov, Manish Bhattarai

CoRR, 2024

LLM-assisted Concept Discovery: Automatically Identifying and Explaining Neuron Functions.

Nhat Hoang-Xuan, Minh N. Vu, My T. Thai

CoRR, 2024

CHARME: A chain-based reinforcement learning approach for the minor embedding problem.

Hoang M. Ngo, Nguyen Hoang Khoi Do, Minh N. Vu, Tamer Kahveci, My T. Thai

CoRR, 2024

Analysis of Privacy Leakage in Federated Large Language Models.

Minh N. Vu, Truc D. T. Nguyen, Tre' R. Jeter, My T. Thai

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Limitations of Perturbation-based Explanation Methods for Temporal Graph Neural Networks.

Minh N. Vu, My T. Thai

Proceedings of the IEEE International Conference on Data Mining, 2023

Active Data Reconstruction Attacks in Vertical Federated Learning.

Minh N. Vu, Tre' R. Jeter, Raed Alharbi, My T. Thai

Proceedings of the IEEE International Conference on Big Data, 2023

2022

On the Limit of Explaining Black-box Temporal Graph Neural Networks.

Minh N. Vu, My T. Thai

CoRR, 2022

EMaP: Explainable AI with Manifold-based Perturbations.

Minh N. Vu, Huy Q. Mai, My T. Thai

CoRR, 2022

NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee.

Minh N. Vu, Truc D. T. Nguyen, My T. Thai

CoRR, 2022

NeuCEPT: Learn Neural Networks' Mechanism via Critical Neurons with Precision Guarantee.

Minh N. Vu, Truc D. T. Nguyen, My T. Thai

Proceedings of the IEEE International Conference on Data Mining, 2022

An Explainer for Temporal Graph Neural Networks.

Wenchong He, Minh N. Vu, Zhe Jiang, My T. Thai

Proceedings of the IEEE Global Communications Conference, 2022

2021

Evaluating Fake News Detection Models from Explainable Machine Learning Perspectives.

Raed Alharbi, Minh N. Vu, My T. Thai

Proceedings of the ICC 2021, 2021

c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation.

Minh N. Vu, Truc D. T. Nguyen, NhatHai Phan, Ralucca Gera, My T. Thai

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Learning Interpretation with Explainable Knowledge Distillation.

Raed Alharbi, Minh N. Vu, My T. Thai

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Capacity Region and Capacity-Achieving Signaling Schemes for 1-bit ADC Multiple Access Channels in Rayleigh Fading.

Mohammad Ranjbar, Nghi H. Tran, Minh N. Vu, Truyen V. Nguyen, Mustafa Cenk Gursoy

IEEE Trans. Wirel. Commun., 2020

PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks.

Minh N. Vu, My T. Thai

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Optimal Signaling Schemes and Capacity of Non-Coherent Rician Fading Channels With Low-Resolution Output Quantization.

Minh N. Vu, Nghi H. Tran, Dissanayakage G. Wijeratne, Khanh D. Pham, Kye-Shin Lee, Duy H. N. Nguyen

IEEE Trans. Wirel. Commun., 2019

Optimal Signaling Schemes and Capacities of Non-Coherent Correlated MISO Channels Under Per-Antenna Power Constraints.

Minh N. Vu, Nghi H. Tran, Hoang Duong Tuan, Truyen V. Nguyen, Duy H. N. Nguyen

IEEE Trans. Commun., 2019

Evaluating Explainers via Perturbation.

Minh N. Vu, Truc D. T. Nguyen, NhatHai Phan, Ralucca Gera, My T. Thai

CoRR, 2019

Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness.

NhatHai Phan, Minh N. Vu, Yang Liu, Ruoming Jin, Dejing Dou, Xintao Wu, My T. Thai

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Optimal Signaling Scheme and Capacity of Non-Coherent Rician Fading Channels with 1-Bit Output Quantization.

Minh N. Vu, Dissanayakage G. Wijeratne, Nghi H. Tran, Khanh D. Pham

Proceedings of the 2018 IEEE International Conference on Communications, 2018