1 citations · 2 across the 7 of their papers we have counts for
10 papers
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…
Signed Graph Pre-Training and Prompt Learning
Zihan Mei, Rong Pan, Yuzhou Chen +1
Signed graphs arise in trust--distrust networks, financial correlation systems, biological interaction graphs, and many other domains in which edges can be positive or negative and…
MemArchitect: A Policy Driven Memory Governance Layer
Lingavasan Suresh Kumar, Yang Ba, Rong Pan
Persistent Large Language Model (LLM) agents expose a critical governance gap in memory management. Standard Retrieval-Augmented Generation (RAG) frameworks treat memory as passive…
Measuring Dataset Diversity from a Geometric Perspective
Yang Ba, Mohammad Sadeq Abolhasani, Michelle V Mancenido +1
Diversity can be broadly defined as the presence of meaningful variation across elements, which can be viewed from multiple perspectives, including statistical variation and geomet…
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…
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data
Yang Ba, Michelle V. Mancenido, Rong Pan
As machine learning models continue to swiftly advance, calibrating their performance has become a major concern prior to practical and widespread implementation. Most existing cal…
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…
Data Quality in Crowdsourcing and Spamming Behavior Detection
Yang Ba, Michelle V. Mancenido, Erin K. Chiou +1
As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data,…