6 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
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…