The Geometry of Pricing: How We Turned a Prediction Problem Into a Decision Problem
February 5, 2026
Engineering Retention: Interpretable Churn Cohort Discovery for Broadband
January 15, 2026
Table of Contents
Mixed Effects Models: Partial Pooling for Data That Comes in Groups
September 3, 2025
Table of Contents
Generalized Linear Models: The Recipe Behind Linear and Logistic Regression
July 15, 2025
Table of Contents
Building a 'Similar Products' Recommender - A Content-Based Approach
February 7, 2025
Table of Contents
Actor-Critic Methods in Reinforcement Learning - An Introduction
February 3, 2025
Actor-Critic methods are a family of reinforcement learning algorithms that combine the benefits of value-based and policy-based approaches: an actor learns which actions to take, while a critic learns to judge how good those actions were and feeds that judgement back so the actor’s learning signal is far less noisy. This post builds the idea from the ground up: why pure policy gradients are noisy, how a baseline fixes that for free, and why the critic is really just a learned, state-dependent baseline.