· Xiaojing Yang · Statistics
Power Analysis: How Many Experiments Do You Need?
Power analysis connects sample size, effect size, and the chance of detecting a real improvement.
Blog
Foundations and research notes on mathematics, statistics, machine learning, NLP, LLMs, model evaluation, and applied AI systems.
- · Xiaojing Yang · Statistics
Probability Distributions for Machine Learning
How distributions become assumptions about data, labels, errors, and model behavior.
- · Xiaojing Yang · NLP and LLMs
Prompting and Instruction Following
Prompting as task specification, interface design, and evaluation risk.
- · Xiaojing Yang · Statistics
Random Variables, Expectation, and Variance
A practical introduction to random variables, expectation, and variance for AI experiments.
- · 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.