• Skip to primary navigation
  • Skip to content
  • Skip to footer
True Theta True Theta
  • Services
  • About
  • Contact
  • Writing
    • Writing

    • AI Tool Reviews
      • CoreWeave User Experience: A Field Report
      • BentoML
      • MLflow
      • Ray
    • Recommender Systems
      • How to Understand Recommender Systems
      • Evaluating Recommender Systems
    • Machine Learning and Other Topics
      • TrueSkill Part 2: Who is the GOAT?
      • TrueSkill Part 1: The Algorithm
      • Algorithmic Operations: Lessons from Bandit Algorithms
      • When does the Delta Method approximation work?
      • Bias-Variance Trade-Off
      • Information Theory and Entropy
      • Generalized Linear Models
      • Jensen's Inequality
      • The Fisher Information
      • Bayesian Optimization
      • The Exponential Family
      • Motivating the Gini Impurity Metric
      • A Brief Explanation and Application of Gaussian Processes
      • The Trace as a Measure of Complexity
    • Probabilistic Graphical Models
      • Part 1: An Introduction to Probabilistic Graphical Models
      • Part 2: Exact Inference
      • Part 3: Variational Inference
      • Part 4: Monte Carlo Methods
      • Part 5: Learning Parameters of a Bayesian Network
      • Part 6: Learning Parameters of a Markov Network
      • Part 7: Structure Learning
      • Summaries
      • Notation Guide
    • Reinforcement Learning
      • A Reliable Contextual Bandit Algorithm: LinUCB
      • Contextual Bandits as Supervised Learning
      • Reinforcement Learning at Lyft
    • Opinions and Speculations
      • A View into Government Cybersecurity
      • Good Data Science is Mostly Dispatch
    • Linear Algebra
      • Randomized Numerical Linear Algebra
      • The Matrix Inversion Lemma
      • Singular Value Decomposition and the Fundamental Theorem of Linear Algebra
      • The Conjugate Gradient Method
    • Financial Engineering
      • The Copula and 2008
      • The Fundamental Law of Active Management

    Reinforcement Learning

    A Reliable Contextual Bandit Algorithm: LinUCB

    A Reliable Contextual Bandit Algorithm: LinUCB

    DJ Rich
    Posted: August 06, 2024

    In this post, we learn about contextual bandits and the reliable linUCB algorithm.

    Contextual Bandits as Supervised Learning

    Contextual Bandits as Supervised Learning

    DJ Rich
    Posted: June 28, 2024

    This posts explains contextual bandits as a generalization of supervised learning.

    Reinforcement Learning at Lyft

    Reinforcement Learning at Lyft

    DJ Rich
    Posted: January 04, 2024

    A few comments on the Reinforcement Learning work done by my colleagues at Lyft

    Social Media

    • YouTube
    • Twitter
    • GitHub
    • LinkedIn
    Want articles emailed to you?
    Join the email list
    Think we can help?
    Contact Us
    • Twitter
    • YouTube
    • GitHub
    • linkedin
    © 2025 DJ Rich.