1 citations · 2 across the 13 of their papers we have counts for
13 papers
RAPTOR: Role-Aware Private Training for Mixture-of-Experts
Duc Dm, Khai Le-Duc, Nguyen Do +18
Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see all data while experts only se…
CutBackdoor: A Circuit Cut Triggered Backdoor Attack on Variational Quantum Algorithms
Ahatesham Bhuiyan, Hoang Ngo, Cheng Chu +4
Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical optimization across quantum ch…
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
Hoang M. Ngo, Quan Nguyen, Wanli Xing +1
Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due to its simplicity and low co…
Semantic Optimal Transport for Sparse Autoencoder Feature Matching and Circuit Compression
Tue M. Cao, Nguyen Do, My T. Thai
Sparse autoencoders (SAEs) have become a central tool for interpreting language models. However, two key SAE analyses that remain difficult to scale are (1) matching semantically s…
AvAtar: Learning to Align via Active Optimal Transport
Qi Yu, Ruizhong Qiu, Zhichen Zeng +3
Alignment plays a fundamental role in many machine learning problems, such as multi-network analysis, multimodal learning, and point cloud registration. Recent works increasingly l…
Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
Hoang M. Ngo, Nhat Hoang-Xuan, Quan Nguyen +3
Quantum Machine Learning (QML) promises significant computational advantages, but preserving training data privacy remains challenging. Classical approaches like differentially pri…
Guaranteeing Privacy in Hybrid Quantum Learning through Theoretical Mechanisms
Hoang M. Ngo, Tre' R. Jeter, Incheol Shin +3
Quantum Machine Learning (QML) is becoming increasingly prevalent due to its potential to enhance classical machine learning (ML) tasks, such as classification. Although quantum no…
QUPID: A Partitioned Quantum Neural Network for Anomaly Detection in Smart Grid
Hoang M. Ngo, Tre' R. Jeter, Jung Taek Seo +1
Smart grid infrastructures have revolutionized energy distribution, but their day-to-day operations require robust anomaly detection methods to counter risks associated with cyber-…
FIDDLE: Reinforcement Learning for Quantum Fidelity Enhancement
Hoang M. Ngo, Tamer Kahveci, My T. Thai
Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing…
CHARME: A chain-based reinforcement learning approach for the minor embedding problem
Hoang M. Ngo, Nguyen H K. Do, Minh N. Vu +3
Quantum annealing (QA) has great potential to solve combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms is heavily based on the embedding o…
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
Nguyen Do, Tanmoy Chowdhury, Chen Ling +2
Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one…
QOMIC: Quantum optimization for motif identification
Hoang M. Ngo, Tamim Khatib, My T. Thai +1
Network motif identification problem aims to find topological patterns in biological networks. Identifying non-overlapping motifs is a computationally challenging problem using cla…
On the Communication Complexity of Decentralized Stochastic Bilevel Optimization
Yihan Zhang, My T. Thai, Jie Wu +1
Stochastic bilevel optimization finds widespread applications in machine learning, including meta-learning, hyperparameter optimization, and neural architecture search. To extend s…