7 papers
When Does Quality-Aware Multimodal Fusion Matter? A Leakage-Safe Diagnostic for Decision-Level Dependence
Jaden Moon, Arvind Pillai, Andrew Campbell
Many multimodal systems estimate the reliability of each modality and weight their contributions to the final prediction. However, it remains unclear whether these scores influence…
Reconstruction Limits for Repeated Differentially Private Aggregates: A Cramer-Rao Perspective on Query Geometry
Chenyue Zhang, Andrew Campbell, Anna Scaglione +1
Repeated differentially private (DP) releases are often evaluated by transcript length or cumulative privacy accounting. We show that these quantities do not by themselves determin…
Graph Transfer Learning via Shared Latent Geometry: Theory and Applications
Tong Wu, Andrew Campbell, Anna Scaglione
Inference and control in engineered physical systems pay a heavy physics cost at deployment: state estimators, inverse-problem solvers, model-predictive controllers, schedulers, an…
Differentially Private Synthetic Voltage Phasor Release for Distribution Grids
Andrew Campbell, Chenyue Zhang, Anna Scaglione +3
Training machine learning models, including Grid Foundation Models (GFMs), requires large volumes of realistic grid data, yet substantial privacy concerns discourage utilities and…
Differential Privacy of Network Parameters from a System Identification Perspective
Andrew Campbell, Anna Scaglione, Hang Liu +3
This paper addresses the problem of protecting network information from privacy system identification (SI) attacks when sharing cyber-physical system simulations. We model analyst…
Decentralized Differentially Private Power Method
Andrew Campbell, Anna Scaglione, Sean Peisert
We propose a novel Decentralized Differentially Private Power Method (D-DP-PM) for performing Principal Component Analysis (PCA) in networked multi-agent settings. Unlike conventio…