Graphic is from the paper “LAVA: Data Valuation Without Pre-Specified Learning Algorithms”
Hoang Anh Just has received some good news: The paper “LAVA: Data Valuation Without Pre-Specified Learning Algorithms” — on which he is first author — has been accepted as a spotlight at the 11th International Conference on Learning Representations (ICLR) in May. He plans to travel to Rwanda to present the paper.
Just, a Ph.D. student in the Bradley Department of Electrical and Computer Engineering, said the paper introduces a new perspective on valuating data.
“For many current valuation methods, the valuation algorithm is based on a model learning process, which is expensive, noise-sensitive, and often impractical. To overcome such hurdles, we valuate data via optimal transport, which requires no model training,” he said. “As such, our data-centric, model-agnostic method effectively detects ‘bad’ data points in the dataset in an efficient manner.”
An interest in artificial intelligence drew him to Virginia Tech and the Sanghani Center. “I am honored to be part of an expanding community that is tackling modern AI problems and pushing the field to greater heights,” Just said.
Just’s advisor, Ruoxi Jia, influenced his research area by introducing him to data evaluation.
“I really found it intriguing that data are used all around, but we barely know their actual value,” he said, “and this led to my work in establishing efficient and fair methods for valuating data used in machine learning models.”
Just received a bachelor’s degree in computer science and mathematics from Gettysburg College.
Projected to graduate in 2026, his goal is to become a professor who can continue research in data valuation and inspire students to conduct research in artificial intelligence.
