🔄 The Natural Connection Between Complexity, Information Theory, and Conformal Prediction
What do compression, randomness, and prediction have in common?
More than you’d expect — and the connections run deep.
In this post, I’ll walk through how three powerful concepts from different corners of mathematics and machine learning converge naturally:
🔢 Kolmogorov Complexity: How compressible is a string?
📉 Information Theory: How much uncertainty is in the data?
📈 Conformal Prediction: How confidently can we make distribution-free predictions?
