1. Introduction
  2. Current Focus
  3. Recommender Systems
    1. Recent Trends
    2. Gradient Boosting
    3. TF-IDF
    4. Cross Encoders
    5. SentenceTransformers
    6. Collaborative Filtering
    7. Evaluation
  5. AB Testing
    1. Examples
    2. Power Analysis
  7. LLMs
    1. Auto Prompt Optimization
    2. Fine-tuning
    3. Useful Models
    4. Encoder vs Decoder
    5. Contextualized Recommendations
    6. MinGPT
  9. Statistics
    1. Conformal Predictions
  11. Miscellaneous
    1. Bradley-Terry Model
    2. Setting up WSL
    3. To Read
    4. Packages
    5. Skills
    6. Hash Collisions
    7. IMO 2025 Q6
  13. Identities
    1. Sigmoid
    2. Statistics
  15. Papers
    1. Weinberger 2009 - Hashing for Multitask Learning
    2. Rendle 2009 - Bayesian Personalized Ranking
    3. Burges 2010 - RankNET to LambdaMART
    4. Schroff 2015 - FaceNET
    5. Covington 2016 - Deep NNs for Youtube Recs
    6. Schnabel 2016 - Recs as Treatments
    7. Doersch 2016 - Tutorial on VAEs
    8. Bateni 2017 - Affinity Clustering
    9. Guo 2017 - DeepFM
    10. Hamilton 2017 - GraphSAGE
    11. Ma 2018 - Entire Space Multi-Task Model
    12. Kang 2018 - SASRec
    13. Reimers 2019 - Sentence-BERT
    14. Yi 2019 - LogQ Correction for In Batch Sampling
    15. Zhao 2019 - Recommending What to Watch Next
    16. Lee 2020 - Large Scale Video Representation Learning
    17. He 2020 - LightGCN
    18. Lewis 2020 - Retrieval Augmented Generation
    19. Wang 2020 - DCNv2
    20. Gao 2021 - GradCache
    21. Gao 2021 - SimCSE
    22. Weng 2021 - Contrastive Representation Learning
    23. Li 2021 - TaoBao Embedding-Based Retrieval
    24. Zou 2021 - PLM Based Ranking in Baidu Search
    25. Dao 2022 - Flash Attention
    26. Wei 2022 - CoT Prompting in LLMs
    27. Honovich 2022 - Instruction Induction
    28. Huang 2022 - LLMs can Self Improve
    29. Tunstall 2022 - SetFit
    30. Wang 2022 - Self Consistency LLM
    31. Lee 2022 - RQ-VAE
    32. Tay 2022 - Differentiable Search Index
    33. Rafailov 2023 - Direct Preference Optimization
    34. Blecher 2023 - Nougat
    35. Dong 2023 - MINE Loss
    36. Liu 2023 - Meaning Representations from Trajectories
    37. Klenitskiy 2023 - BERT4Rec vs SASRec
    38. Singh 2023 - Semantic IDs for Recs
    39. Yang 2023 - OPRO
    40. Rajput 2023 - Generative Retrieval
    41. Borisyuk 2024 - GNN at LinkedIn
    42. Wang 2024 - LLM for Pinterest Search
    43. Solatorio 2024 - GISTEmbed
    44. Sanjabi 2025 - 360Brew
    45. Zhang 2025 - Qwen3 Embedding
  17. Talks
    1. Yan 2025 - LLM for Recsys
    2. Tandon 2025 - Gemini for YouTube
    3. Hameed 2025 - 360Brew
  19. NLP Course
  20. Database Course
    1. Lecture 1
    2. Lecture 2
    3. Lecture 3
  22. RL Course
    1. Introduction
    2. MDPs
    3. Dynamic Programming
    4. Model Free Prediction
    5. Model Free Control
    6. Value Function Approximation
    7. Policy Gradient
    8. Learning & Planning