14 citations · 26 across the 6 of their papers we have counts for
5 papers · 1 filter
Bridging the Divide: End-to-End Sequence-Graph Learning
Yuen Chen, Yulun Wu, Samuel Sharpe +5
Many real-world prediction tasks, particularly those involving entities such as customers or patients, involve both {sequential} and {relational} data. Each entity maintains its ow…
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence
Isha Hameed, Samuel Sharpe, Daniel Barcklow +5
Explainable artificial intelligence (XAI) methods lack ground truth. In its place, method developers have relied on axioms to determine desirable properties for their explanations'…
Counterfactual Explanations via Latent Space Projection and Interpolation
Brian Barr, Matthew R. Harrington, Samuel Sharpe +1
Counterfactual explanations represent the minimal change to a data sample that alters its predicted classification, typically from an unfavorable initial class to a desired target…
Dynamic Customer Embeddings for Financial Service Applications
Nima Chitsazan, Samuel Sharpe, Dwipam Katariya +2
As financial services (FS) companies have experienced drastic technology driven changes, the availability of new data streams provides the opportunity for more comprehensive custom…
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations
Rachana Balasubramanian, Samuel Sharpe, Brian Barr +2
In the environment of fair lending laws and the General Data Protection Regulation (GDPR), the ability to explain a model's prediction is of paramount importance. High quality expl…