· Xiaojing Yang · Statistics
Random Variables, Expectation, and Variance
A practical introduction to random variables, expectation, and variance for AI experiments.
Blog
Foundations and research notes on mathematics, statistics, machine learning, NLP, LLMs, model evaluation, and applied AI systems.
- · Xiaojing Yang · Statistics
Regularization: Statistics Behind L1 and L2
Regularization controls model complexity by making some parameter values less plausible.
- · Xiaojing Yang · NLP and LLMs
Sequence-to-Sequence Models
The encoder-decoder idea behind machine translation, summarization, and many generation tasks.
- · Xiaojing Yang · Statistics
Statistical Testing for NLP and LLM Evaluation
A practical map for choosing statistical tests in NLP, MT, RAG, and LLM evaluation.
- · Xiaojing Yang · NLP and LLMs
Tokenization and Subwords
Tokenization is the first modeling decision in NLP: it decides what units a model can see, how rare terms are represented, and how multilingual systems handle domain language.
- · Xiaojing Yang · Machine Learning
Train / Validation / Test Splits
A practical guide to splitting data so model evaluation stays honest.