99 citations · 111 across the 10 of their papers we have counts for
13 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…
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…
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,…
PADTHAI-MM: Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology
Myke C. Cohen, Nayoung Kim, Yang Ba +7
Despite an extensive body of literature on trust in technology, designing trustworthy AI systems for high-stakes decision domains remains a significant challenge, further compounde…
Evaluating Trustworthiness of AI-Enabled Decision Support Systems: Validation of the Multisource AI Scorecard Table (MAST)
Pouria Salehi, Yang Ba, Nayoung Kim +10
The Multisource AI Scorecard Table (MAST) is a checklist tool based on analytic tradecraft standards to inform the design and evaluation of trustworthy AI systems. In this study, w…
Robust Stance Detection: Understanding Public Perceptions in Social Media
Nayoung Kim, David Mosallanezhad, Lu Cheng +2
The abundance of social media data has presented opportunities for accurately determining public and group-specific stances around policy proposals or controversial topics. In cont…
Domain Adaptive Fake News Detection via Reinforcement Learning
Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu +2
With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate…
"Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?
Mansooreh Karami, Ahmadreza Mosallanezhad, Michelle V Mancenido +1
Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities.…
Toward Privacy and Utility Preserving Image Representation
Ahmadreza Mosallanezhad, Yasin N. Silva, Michelle V. Mancenido +1
Face images are rich data items that are useful and can easily be collected in many applications, such as in 1-to-1 face verification tasks in the domain of security and surveillan…