readme
Useful information about my research interests.
Books on Machine Learning Theory and Related Topics 🔍
For those interested in the theory of Machine Learning, I’ve compiled a selection of books that I find very cool. If this is your first contact with this subject, I recommend prioritizing the books highlighted with ★.
- A probabilistic theory of pattern recognition - L. Devroye, G. Lugosi, L. Györfi
- Combinatorial Methods in Density Estimation - L. Devroye, G. Lugosi
- Concentration Inequalities - S. Boucheron, G. Lugosi, P. Massart
- Concentration of Measure for the Analysis of Randomized Algorithms - D. Dubhashi, A. Panconesi
- ★ Foundations of Machine Learning - M. Mohri, A. Rostamizadeh, A. Talwalkar
- ★ High-Dimensional Probability - R. Vershynin
- Learning Theory from First Principles - F. Bach
- The Concentration of Measure Phenomenon - M. Ledoux
- ★ Understanding Machine Learning - S. Shalev-Shwartz, S. Ben-David
Basic Books on Probability and Statistics 🤓
If you’re new to Probability and Statistics, here are some great books to get you started.
- All of Nonparametric Statistics - L. Wasserman
- All of Statistics: A Concise Course in Statistical Inference - L. Wasserman
- An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Introduction to Probability - J. Blitzstein, J. Hwang
- Probability with Martingales - D. Williams
- Probability: Theory and Examples - R. Durrett
- The Elements of Statistical Learning - T. Hastie, R. Tibshirani, J. Friedman
- Theoretical Statistics: Topics for a Core Course - R. Keener
Favorite Papers 😍
Some of my favorite papers in no particular order.
- A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting☆ - Y. Freund, R. E. Shapire
- Finding community structure in networks using the eigenvectors of matrices - M. E. Newman
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation - M. Belkin, P. Niyogi
- Strong Consistency, Graph Laplacians, and the Stochastic Block Model - S. Deng, S. Ling, T. Strohmer
Online Material 🌐
Cool online material