18 citations · 28 across the 16 of their papers we have counts for
5 papers · 1 filter
An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees
Hoang Tran, Jorge Ramirez, Jiayi Wang +3
Fine-tuning adapts a pretrained machine learning model to a small, sensitive dataset, but this process risks memorizing individual new data points, making the model vulnerable to a…
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
Xue Feng, M. Paul Laiu, Thomas Strohmer
Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data…
MATEY: multiscale adaptive foundation models for spatiotemporal physical systems
Pei Zhang, M. Paul Laiu, Matthew Norman +2
Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer (ViT) architectures requires extremely long, computationally prohibit…
Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees
Steffen Schotthöfer, M. Paul Laiu
In this work, we propose a federated dynamical low-rank training (FeDLRT) scheme to reduce client compute and communication costs - two significant performance bottlenecks in horiz…
Streaming Compression of Scientific Data via weak-SINDy
Benjamin P. Russo, M. Paul Laiu, Richard Archibald
In this paper a streaming weak-SINDy algorithm is developed specifically for compressing streaming scientific data. The production of scientific data, either via simulation or expe…