Research Archive

This paper is a particular favorite of mine. Sinha and Mahajan break down the core of sequential decision-making.

A primer on entropy, mutual information, and variational inference.

We propose an MDL-based measure of network structural complexity. The method utilizes the two-phase MDL approach for describing random and nonrandom variation.

The third installment in our Foundations series, where we connect sets and sigma algebras to the actual rules of probability.

Continuing our Foundations series, we explore sigma-algebras—the mathematical scaffolding that makes probability rigorous and connects abstract theory to real-world randomness in our models.

As part of our Foundations series, we talk about the foundations of Machine Learning. This section discusses set theory and its elegant applications to everything from LLMs to recommender systems.

Notes from the seminal Sekhari et al paper. We break down the state of machine unlearning and what makes their method particularly attractive.