10 papers · 1 filter
Power Transform Revisited: Numerically Stable, and Federated
Xuefeng Xu, Graham Cormode
Power transforms are popular parametric methods for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However…
FedPS: Federated data Preprocessing via aggregated Statistics
Xuefeng Xu, Graham Cormode
Federated Learning (FL) enables multiple parties to collaboratively train machine learning models without sharing raw data. However, before training, data must be preprocessed to a…
GEM+: Scalable State-of-the-Art Private Synthetic Data with Generator Networks
Samuel Maddock, Shripad Gade, Graham Cormode +1
State-of-the-art differentially private synthetic tabular data has been defined by adaptive 'select-measure-generate' frameworks, exemplified by methods like AIM. These approaches…
Federated Computation of ROC and PR Curves
Xuefeng Xu, Graham Cormode
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental tools for evaluating machine learning classifiers, offering detailed insights into the trad…
Private Federated Multiclass Post-hoc Calibration
Samuel Maddock, Graham Cormode, Carsten Maple
Calibrating machine learning models so that predicted probabilities better reflect the true outcome frequencies is crucial for reliable decision-making across many applications. In…
Synthetic Tabular Data: Methods, Attacks and Defenses
Graham Cormode, Samuel Maddock, Enayat Ullah +1
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much…