7 papers · 1 filter
Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
Yvonne Zhou, Mingyu Liang, Ivan Brugere +5
We present the first theoretical convergence analysis of machine learning training under fully homomorphic encryption (FHE), combined with a differentially private (DP) training al…
Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
Wenzhuo Tang, Haitao Mao, Danial Dervovic +4
Models for natural language and images benefit from data scaling behavior: the more data fed into the model, the better they perform. This 'better with more' phenomenon enables the…
ELATE: Evolutionary Language model for Automated Time-series Engineering
Andrew Murray, Danial Dervovic, Michael Cashmore
Time-series prediction involves forecasting future values using machine learning models. Feature engineering, whereby existing features are transformed to make new ones, is critica…
Model Evaluation in the Dark: Robust Classifier Metrics with Missing Labels
Danial Dervovic, Michael Cashmore
Missing data in supervised learning is well-studied, but the specific issue of missing labels during model evaluation has been overlooked. Ignoring samples with missing values, a c…
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Yvonne Zhou, Mingyu Liang, Ivan Brugere +4
The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset. To pre…
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness
Zachary McBride Lazri, Ivan Brugere, Xin Tian +4
Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many…