3 papers
cs.CR2026
zkComposer: Decomposing Proof Construction to Scale zkML
Pawan Kumar Sanjaya, Christina Giannoula, Valdy Oktavian +4
Zero-knowledge machine learning (zkML) enables a server to perform verifiable inference while keeping model parameters private from the client. However, existing zkML systems incur…
cs.AR2026
DataGuard: Guaranteeing Private Training in Systolic-array Based Accelerators
Pawan Kumar Sanjaya, Christina Giannoula, Nikhil Shreekumar +6
Differential privacy (DP) and federated learning (FL) have emerged as important privacy-preserving approaches when using sensitive data to train machine learning (ML) models. FL en…
cs.GR2026
XPR: An Extensible Cross-Platform Point-Based Differentiable Renderer
Steve Rhyner, Sankeerth Durvasula, Aleksandr Kovalev +7
Point-based differentiable rendering underpins modern 3D reconstruction, novel-view synthesis, and learning-based graphics pipelines, but developing new rendering methods often req…