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