no code implementations • 5 Oct 2022 • I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson
Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today.
no code implementations • 20 Oct 2020 • Sahil Verma, Varich Boonsanong, Minh Hoang, Keegan E. Hines, John P. Dickerson, Chirag Shah
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders.
no code implementations • 18 Jun 2020 • Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen, Reza Sarshogh, Keegan E. Hines
Graph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks.
no code implementations • 7 Oct 2019 • Antonia Gogoglou, C. Bayan Bruss, Keegan E. Hines
With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties.
no code implementations • 16 Jul 2019 • C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Antonia Gogoglou, Keegan E. Hines
In this paper, we present a novel application of representation learning to bipartite graphs of credit card transactions in order to learn embeddings of account and merchant entities.
no code implementations • 3 Jul 2019 • C. Bayan Bruss, Anish Khazane, Jonathan Rider, Richard Serpe, Saurabh Nagrecha, Keegan E. Hines
Graph embedding is a popular algorithmic approach for creating vector representations for individual vertices in networks.
no code implementations • 21 Jun 2019 • Mohammad Reza Sarshogh, Keegan E. Hines
We present an end-to-end trainable multi-task network that addresses the problem of lexicon-free text extraction from complex documents.