📃: Paper, 📖: Book, 🔗: Posting, 🎞️: Youtube. 

Statistics

1. 📃 Statistical Science (Journal)

2. 🔗 Variational Bayes and the Mean-field Approximation. Brian Keng.  

Machine Learning

• Forecasting

1. 📃 International Journal of Forecasting (Journal)

2. 📃 Forecasting with trees. Tim Januschowski et al., International Journal of Forecasting, 2022.

3. 📃 Forecasting at scale (Prophet) Taylor, S. J., & Letham, B, The American Statistician, 2018.

• Tree-based Model

1. 📃 Autoencoding Random Forests. Bin Duc Vu et al., preprint, 2025.

• Deep Learning

  1. 📖 Understanding Deep Learning. Simon Prince.  

• Decision-Focused Learning 

  1. 📃 Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities. Survey paper.

Generative Model

• Variational Autoencoder

1. 🔗 Tutorial #5: variational autoencoders. Simon Prince.

2. 📃 An introduction Variational Autoencoders. Dierik P. Kingma, Max Welling. 

3. 🔗 The reparameterization trick: why? Prof. Jaejun Yoo (in Korean). 

4. 🔗 VAE series by Prof. Jakub M. Tomczak.

   1. 🔗 Varitional Auto-Encoders.  

   2. 🔗 Priors in VAEs.

   3. 🔗 Hierarchical VAEs. 

• Normalizing Flows

1. 🔗 Tutorial, Part 1: Distributions and Determinants. Eric Jang.

2. 🔗 Tutorial, Part 2: Modern Normalizing Flow. Eric Jang.

Finance

1. 🔗 AI in Finance. researchtrend.ai.  

2. 📃 Latent Variable Estimation in Bayesian Black-Litterman Models. Lin, T., et al., ICML, 2025.

3. 📃 Forecasting Company Fundamentals. Divo, F, et al., TMLR, 2025.

Optimization

1. 🔗 Natural Gradients. Andy Jones.  

Data Analysis Competitions

1. Data Competition by Main Conferences  (NeurIPS, KDD, WWW, CIKM, WSDM, ICDM, IEEE BigData, ...)

2. 🔗 Kaggle: global data analysis competition.

3. 🔗 Dacon: data analysis competition.

4. 🔗 COMPAS: spatial (urban) data analysis competition by LH. 

5. 🔗 AIFactory: data analysis competition.