Shreyas Fadnavis
Conformal Prediction
Your model gives you a number. How wrong could it be? This sequence starts with that question and traces a path through distribution-free prediction intervals, the impossibility of conditional coverage, variance stabilization, adaptive methods, and what happens when the foundational assumption of exchangeability breaks down.
Leverage Scores
A single diagonal of a matrix tells you where your model is extrapolating, how sensitive each prediction is to the training data, and why some points matter more than others. This sequence builds from the classical hat matrix through regression diagnostics, the sign flip that most methods get wrong, and onward to randomized algorithms and high-dimensional extensions.
Agentic AI
One model hallucinates. Multiple models, organized into a collaborative architecture, can cross-check, correct, and refine each other. This sequence traces the theory from ensemble error decomposition and Condorcet's Jury Theorem through Mixture of Agents, social choice theory, reasoning architectures, agentic retrieval, and the scaling laws that govern when more agents help and when they don't.