The Twynvex Blog
Technical writing on distribution tail coverage, privacy-by-construction synthetic generation, and tabular data synthesis. Written by Lars Bergstrom and Priya Anand from the Twynvex team.
Why Edge Cases Break ML Models (And What to Do About It)
Your model is accurate on 99% of your test set. Then it fails in production on the 1% that matters. Here's the distribution theory behind why — and how synthetic tail generation changes the equation.
When Privacy Rules Block Your Training Data: A Practical Approach
HIPAA, GDPR, data governance policies — real-world constraints that block ML teams from using real records. Privacy-by-construction synthetic generation is the practical path through.
Distribution Matching in Synthetic Data: What It Means and Why It Matters
Naive random generation produces useless training data. Real synthetic generation preserves marginal distributions and pairwise correlations. Here's the math behind why it matters for your model.
Labeling at Scale: Why the Synthetic Approach Beats Manual Annotation
Manual annotation is slow, expensive, and can't cover rare classes by definition. Synthetic generation flips the problem: labels come first, rows are generated to match.
Structured Tabular Data Generation: Harder Than It Looks
Tabular data has categorical columns, foreign keys, business rules, and heterogeneous types. Synthesizing it correctly is a constraint satisfaction problem, not just a sampling problem.
Synthetic Data vs. Data Augmentation: Picking the Right Tool
Augmentation transforms existing real data. Synthetic generation creates new rows from a learned distribution. They solve different problems — here's how to choose.