activity
20222025
most citedA Survey of Machine Unlearning

68 citations · 71 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025★ 1 cited

ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge

Manh Cuong Dao, The Hung Tran, Phi Le Nguyen +2

This paper studies the black-box optimization task which aims to find the maxima of a black-box function using a static set of its observed input-output pairs. This is often achiev…

quant-ph2025

Expressive and Scalable Quantum Fusion for Multimodal Learning

Tuyen Nguyen, Trong Nghia Hoang, Phi Le Nguyen +2

The aim of this paper is to introduce a quantum fusion mechanism for multimodal learning and to establish its theoretical and empirical potential. The proposed method, called the Q…

cs.CV2024

Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era

Thanh Tam Nguyen, Zhao Ren, Trinh Pham +4

The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require si…

cs.CV2022

A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

Trung Thanh Nguyen, Hoang Dang Nguyen, Thanh Hung Nguyen +3

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies…

cs.LG2022★ 68 cited

A Survey of Machine Unlearning

Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren +4

Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in artificial intelligence, and especially machine learning (M…

cs.LG2022★ 2 cited

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

Nang Hung Nguyen, Phi Le Nguyen, Duc Long Nguyen +4

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning…