Lecture notes are taken by students. The scribe is expected to verify the correctness of all proofs, to fill in any obvious gaps in the lecture, and to add figures and illustrative images as needed. Please use the provided .sty file, and follow the style and notation conventions from the sample document.
Please sign-up to scribe for a lecture using this sign-up spreadsheet.
Most of these notes have been posted as submitted, without careful checking by the instructors; feel free to contact us with any questions or potential issues with these notes.
| Title | File | |
| 1 | Introduction to Online Learning | |
| 2 | Online Convex Optimization and Follow the Leader | |
| 3 | Follow-The-Regularized-Leader | |
| 4 | Convexity and Online Gradient Descent | |
| 5 | FTRL with Arbitrary Strongly Convex Regularization and Experts | |
| 6 | Online Learning with Expert Advice | |
| 7 | Exponentiated Gradient and Bandits | |
| 8 | The Multi-Armed Bandit Problem | |
| 9 | Tight EXP3 analysis | |
| 10 | Bandits with Expert Advice: From EXP3 to EXP4 | |
| 11 | Analyzing Adaptive Algorithms I | |
| 12 | Analyzing Adaptive Algorithms II | |
| 13 | Adaptive Algorithms III | |
| 14 | Stochastic Experts and Bandits | |
| 15 | Stochastic bandits: Explore-First and UCB | |
| 16 | Bandit Online Convex Optimization | |
| 17 | Combinatorial Bandits | |
| 18 | Minimax Optimal Algorithms | |
| 19 | Information-Theoretic Lower Bounds | |
| 20 | CTR Predictions and Literature References (References) (CTR Prediction Talk) |