· Xiaojing Yang · Explainability and Responsible AI
Data Intervention and Attribution Validation
How to validate data attribution through deletion, correction, reweighting, counterfactual examples, retraining, and random deletion baselines.
Blog
Foundations and research notes on mathematics, statistics, machine learning, NLP, LLMs, model evaluation, and applied AI systems.
- · Xiaojing Yang · Explainability and Responsible AI
Instance-Level Attribution: Gradient Similarity, Influence Functions, and TracIn
A practical comparison of instance-level attribution methods for NLP: gradient similarity, influence functions, and TracIn, including assumptions and limitations.
- · Xiaojing Yang · Explainability and Responsible AI
Interview Notes: Explaining My Training-Data Attribution Thesis
A compact interview narrative for a training-data attribution thesis: research question, method, contribution, limitations, and PhD extensions.
- · Xiaojing Yang · Explainability and Responsible AI
Scaling Data Attribution: Monte Carlo Shapley, Surrogate Models, and Hierarchical Attribution
Why exact Shapley is expensive and how scalable attribution uses sampling, surrogate models, datamodels, and group-to-document-to-example hierarchies.
- · Xiaojing Yang · Explainability and Responsible AI
Statistical Reliability: Bias, Variance, Bootstrap, and Attribution Uncertainty
Attribution scores are estimates. This note separates estimator bias, sampling variance, training randomness, evaluation uncertainty, and bootstrap confidence intervals.
- · Xiaojing Yang · Explainability and Responsible AI
Training Data Attribution for NLP Research
A research-oriented guide to training-data attribution: attribution units, utility functions, Shapley values, influence methods, causality, scalability, and uncertainty.