6 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
PLDiv: Measuring Dataset Diversity and Semantic Collapse via Persistence Landscapes
Yang Ba, Michelle V. Mancenido, Rong Pan
Modern data pipelines increasingly combine human-created and human-curated data with AI-generated data for model training and evaluation. As datasets grow, they often add semantica…
Can We Trust This Evaluation? Instance-Level Reliability of LLM-as-a-Judge
Yang Ba, Michelle V. Mancenido, Rong Pan
LLM-as-a-judge evaluation commonly aggregates multiple judges' decisions into a majority vote, yet this panel decision process does not guarantee that the evaluation of an individu…
Training-Aware Target Coverage for Synthetic Data Selection
Yang Ba, Michelle V. Mancenido, Rong Pan
Synthetic data are increasingly used to scale LLM training, yet more synthetic data do not necessarily produce better models. Useful synthetic data must add information relevant to…
Predict Training Data Quality via Its Geometry in Metric Space
Yang Ba, Mohammad Sadeq Abolhasani, Rong Pan
High-quality training data is the foundation of machine learning and artificial intelligence, shaping how models learn and perform. Although much is known about what types of data…
Data Diversity as Implicit Regularization: How Does Diversity Shape the Weight Space of Deep Neural Networks?
Yang Ba, Michelle V. Mancenido, Rong Pan
Data augmentation that introduces diversity into the input data has long been used in training deep learning models. It has demonstrated benefits in improving robustness and genera…