· Xiaojing Yang · Statistics
Maximum Likelihood Estimation: Why Models Learn Parameters
Maximum likelihood connects probability models to parameter learning.
Blog
Foundations and research notes on mathematics, statistics, machine learning, NLP, LLMs, model evaluation, and applied AI systems.
- · Xiaojing Yang · Machine Learning
Metrics Beyond Accuracy
Accuracy is easy to understand, but often wrong for imbalanced, ranked, or cost-sensitive tasks.
- · Xiaojing Yang · Machine Learning
Model Selection: A Practical Guide
Model selection is the disciplined process of choosing among models without fooling yourself.
- · Xiaojing Yang · Statistics
Multiple Comparisons in AI Experiments
When we try many models, prompts, seeds, and metrics, false discoveries become easier than they look.
- · 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.