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!

Statistics

Classic Papers

Others

Bootstrapping

Machine Learning

No Free Lunch Theorems

DBSCAN

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

Word2vec

Neural Networks

Tree Algorithms, Bagging, Boosting

Others