22 citations · 43 across the 6 of their papers we have counts for
7 papers · 1 filter
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation
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 tod…
Counterfactual Explanations for Machine Learning: Challenges Revisited
Sahil Verma, John Dickerson, Keegan Hines
Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models. They provide ``what if'' feedback of the form `…
Quantifying Challenges in the Application of Graph Representation Learning
Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen +2
Graph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks. Current app…
On the Interpretability and Evaluation of Graph Representation Learning
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. While these approaches have impro…
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools
Anh Truong, Austin Walters, Jeremy Goodsitt +3
There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the i…
DeepTrax: Embedding Graphs of Financial Transactions
C. Bayan Bruss, Anish Khazane, Jonathan Rider +3
Financial transactions can be considered edges in a heterogeneous graph between entities sending money and entities receiving money. For financial institutions, such a graph is lik…