Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
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…
cs.LG2026
FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction
Duc Dm, Thao Do, Minh Son Hoang +3
Differentially private (DP) training protects individual examples by adding noise to gradients, but the injected noise interacts nontrivially with adaptive optimizers. Recent DP me…
cs.LG2026
Knowing When to Answer: Adaptive Confidence Refinement for Reliable Audio-Visual Question Answering
Dinh Phu Tran, Jihoon Jeong, Saad Wazir +4
We present a formal problem formulation for \textit{Reliable} Audio-Visual Question Answering (-AVQA), where we prefer abstention over answering incorrectly. While rec…