536 citations · 882 across the 18 of their papers we have counts for
12 papers · 1 filter
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…
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…
Low Latency Privacy Preserving Inference
Alon Brutzkus, Oren Elisha, Ran Gilad-Bachrach
When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphi…
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
John Moore, Joel Pfeiffer, Kai Wei +5
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models. This introduces a bias in the training data…