Great Papers in Statistics & Machine Learning

This is a list of statistics and machine learning research papers that I found to be interesting or are significant.

Most of these papers had great impact or discussed concepts that had great impact in the fields of statistics and machine learning. In fact, a lot of these papers are regarded as major historical advancements in their respective fields.

The number of citations for each entry is a rough guide, since they are only correct when I first added the entry. I might add brief thoughts and comments for individual papers over time.

I hope you have as much fun as I did exploring them!


Classic Papers



Machine Learning

No Free Lunch Theorems


Naive Bayes Classifier

The Optimality of Naive Bayes - Harry Zhang - 2004 - 2,180 citations

Tackling the Poor Assumptions of Naive Bayes Text Classifiers - Rennie et al - 2003 - 1,445 citations

Estimating Continuous Distributions in Bayesian Classifiers - John and Langley - 1995 - 4,629 citations

Bias-Variance Trade-off

Support Vector Machines


Neural Networks

Tree Algorithms, Bagging, Boosting