1 citations · 2 across the 12 of their papers we have counts for
5 papers · 1 filter
Inner Momentum for Differentially Private Muon
Bishnu Bhusal, Minh Vu, Ben Southworth +3
Differentially private training clips each per-example gradient before adding noise. This clipping is radial for each example, yet unequal clipping factors can distort the relative…
One Model, Two Physical Stories: Auditing Misalignment in Multi-Modal World Modeling
Geigh Zollicoffer, Minh Vu, Rajiv Ranasinghe +1
World models, systems that generate what happens next given current environmental conditions, are increasingly being implemented with multi-modal generation in mind. However, gener…
Trustworthy Agentic AI Requires Deterministic Architectural Boundaries
Manish Bhattarai, Minh Vu
Current agentic AI architectures are fundamentally incompatible with the security and epistemological requirements of high-stakes scientific workflows. The problem is not inadequat…
Mutual Information Collapse Explains Disentanglement Failure in -VAEs
Minh Vu, Xiaoliang Wan, Shuangqing Wei
The -VAE is a foundational framework for unsupervised disentanglement, using to regulate the trade-off between latent factorization and reconstruction fidelity. Empirically,…
Privacy Enhanced PEFT: Tensor Train Decomposition Improves Privacy Utility Tradeoffs under DP-SGD
Pradip Kunwar, Minh Vu, Maanak Gupta +1
Fine-tuning large language models on sensitive data poses significant privacy risks, as membership inference attacks can reveal whether individual records were used during training…