most citedExplainable Clustering via Exemplars: Complexity and Efficient Approximation Algorithms

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2024

Foundations for Unfairness in Anomaly Detection -- Case Studies in Facial Imaging Data

Michael Livanos, Ian Davidson

Deep anomaly detection (AD) is perhaps the most controversial of data analytic tasks as it identifies entities that are then specifically targeted for further investigation or excl…

cs.AI2024

Identification and Uses of Deep Learning Backbones via Pattern Mining

Michael Livanos, Ian Davidson

Deep learning is extensively used in many areas of data mining as a black-box method with impressive results. However, understanding the core mechanism of how deep learning makes p…

cs.LG2024

Cooperative Knowledge Distillation: A Learner Agnostic Approach

Michael Livanos, Ian Davidson, Stephen Wong

Knowledge distillation is a simple but powerful way to transfer knowledge between a teacher model to a student model. Existing work suffers from at least one of the following key l…

cs.AI2022★ 2 cited

Explainable Clustering via Exemplars: Complexity and Efficient Approximation Algorithms

Ian Davidson, Michael Livanos, Antoine Gourru +3

Explainable AI (XAI) is an important developing area but remains relatively understudied for clustering. We propose an explainable-by-design clustering approach that not only finds…

cs.CV2022

Semantic Motion Correction Via Iterative Nonlinear Optimization and Animation

Sairamvinay Vijayaraghavan, Jinxiao Song, Wan-Jhen Lin +1

Here, we present an end-to-end method to create 2D animation for a goalkeeper attempting to block a penalty kick, and then correct that motion using an iterative nonlinear optimiza…