· Xiaojing Yang · Statistics
Multiple Comparisons in AI Experiments
When we try many models, prompts, seeds, and metrics, false discoveries become easier than they look.
Blog
Foundations and research notes on mathematics, statistics, machine learning, NLP, LLMs, model evaluation, and applied AI systems.
- · Xiaojing Yang · NLP and LLMs
NLP Evaluation
Why NLP evaluation needs metrics, uncertainty, human judgment, and task-specific error analysis.
- · Xiaojing Yang · Machine Learning
Overfitting and Regularization
How models learn noise, how validation curves reveal it, and how regularization controls it.
- · Xiaojing Yang · NLP and LLMs
Parameter-Efficient Fine-Tuning
Why LoRA and adapters are useful when full fine-tuning is too expensive or unstable.
- · Xiaojing Yang · Statistics
PCA: From Variance to Representation
PCA explains how variance, projection, and representation are connected.
- · Xiaojing Yang · Machine Learning
Pipelines and Data Leakage
Why preprocessing belongs inside the validation pipeline, not before the split.