📔 Conformal Prediction and Learning

📌 Logistics: This is a self-taught course. The topics are designed by myself mostly following the book: Algorithmic learning in a random world by V.Vovk, A.Gammerman, and G.Shafer.

• Lec-1: Introduction and Probability review.
• Lec-2: Confidence Predictors.
• Lec-3: Conformal Predictors.
• Lec-4: Conformalized Ridge & Nearest Neighbors Regression.
• Lec-5: Efficiency of CRR Algorithm.
• Lec-6: Conformal Transducers - coming soon
• Lec-7: Conformal Prediction: criteria of efficiency - coming soon
• Lec-8: Non-conformity scores - coming soon
• Lec-9: Weak Teachers - coming soon
• Lec-10: Inductive Conformal Predictors - coming soon
• Lec-11: Non-concformity scores: further ways - coming soon
• Lec-12: Cross & Transductive Conformal Predictors - coming soon
• Lec-13: Conditional Conformal Predictors - coming soon
• Lec-14: Training Conditional Validity - coming soon
• Lec-15: Conformal Test Martingles
• Lec-16: Multi-stage Randomness Detection
• Lec-17: Testing in Anti-Causal Classification - coming soon
• Lec-18: Efficiency of Conformal Testing - coming soon
• Lec-19: Further Topics in Test Martingles - coming soon
• Lec-20: Impossibility of Estimation: Binary & Multiclass Case - coming soon
• Lec-21: Online Compression Models - coming soon
• Lec-22: Exchangeability Models - coming soon
• Lec-23: Gaussian Models - coming soon
• Lec-24: Inductive Learning - coming soon
• Lec-25: Transductive Learning - coming soon
• Lec-26: Bayesian Learning and Beyond - coming soon