536 citations · 882 across the 18 of their papers we have counts for
18 papers
The Case Against Explainability
Hofit Wasserman Rozen, Niva Elkin-Koren, Ran Gilad-Bachrach
As artificial intelligence (AI) becomes more prevalent there is a growing demand from regulators to accompany decisions made by such systems with explanations. However, a persisten…
TREE-G: Decision Trees Contesting Graph Neural Networks
Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
When dealing with tabular data, models based on decision trees are a popular choice due to their high accuracy on these data types, their ease of application, and explainability pr…
PyBryt: auto-assessment and auto-grading for computational thinking
Christopher Pyles, Francois van Schalkwyk, Gerard J. Gorman +4
We continuously interact with computerized systems to achieve goals and perform tasks in our personal and professional lives. Therefore, the ability to program such systems is a sk…
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi +1
Motivated by the fact that humans like some level of unpredictability or novelty, and might therefore get quickly bored when interacting with a stationary policy, we introduce a no…
Robust Model Compression Using Deep Hypotheses
Omri Armstrong, Ran Gilad-Bachrach
Machine Learning models should ideally be compact and robust. Compactness provides efficiency and comprehensibility whereas robustness provides resilience. Both topics have been st…
Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural Case
Amnon Catav, Boyang Fu, Jason Ernst +2
When training a predictive model over medical data, the goal is sometimes to gain insights about a certain disease. In such cases, it is common to use feature importance as a tool…