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reading-list

This is a collection of interesting papers that I have read so far or want to read. Note that the list is not up-to-date.

Table of Contents

  1. General Deep Learning
  2. Conformal Prediction
  3. Differential Geometry in Deep Learning
  4. Dimensionality Reduction
  5. Thompson Sampling
  6. Deep Reinforcement Learning
  7. Reinforcement Learning
  8. Bandit algorithms
  9. Optimization
  10. Statistics
  11. Probability modeling and inference
  12. Books, courses and lecture notes
  13. Blogs and tutorial
  14. Schools

1. General deep learning

2. Conformal prediction

3. Differential geometry in deep learning

4. Dimensionality reduction

5. Thompson sampling

6. Deep Reinforcement Learning

7. Reinforcement Learning

8. Bandit algorithms

9. Optimization

Min-max optimization

10. Statistics

11. Probability modeling, inference

12. Lecture notes, books and courses

13. Blogs

14. Schools

Papers to add

Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks https://proceedings.neurips.cc/paper/2016/file/abd815286ba1007abfbb8415b83ae2cf-Paper.pdf