1 citations · 2 across the 7 of their papers we have counts for
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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…