Publications (7)
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…
Hephaestus: Mixture Generative Modeling with Energy Guidance for Large-scale QoS Degradation
Nguyen Do, Bach Ngo, Youval Kashuv +3
We study the Quality of Service Degradation (QoSD) problem, in which an adversary perturbs edge weights to degrade network performance. This setting arises in both network infrastr…
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models
Nguyen Do, Truc Nguyen, Malik Hassanaly +3
Despite a plethora of anomaly detection models developed over the years, their ability to generalize to unseen anomalies remains an issue, particularly in critical systems. This pa…
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…
REM: A Scalable Reinforced Multi-Expert Framework for Multiplex Influence Maximization
Huyen Nguyen, Hieu Dam, Nguyen Do +2
In social online platforms, identifying influential seed users to maximize influence spread is a crucial as it can greatly diminish the cost and efforts required for information di…
SPHINX: First Explain, Then Explore
Nguyen Do, Tue M. Cao, Tien Van Do +3
Generating adversarial driving scenarios is critical for evaluating and improving autonomous vehicle decision-making systems in simulation. Recent approaches rely primarily on the…