7 papers
Additive Models Explained: A Computational Complexity Approach
Shahaf Bassan, Michal Moshkovitz, Guy Katz
Generalized Additive Models (GAMs) are commonly considered *interpretable* within the ML community, as their structure makes the relationship between inputs and outputs relatively…
Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations
Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner +2
Despite significant advancements in post-hoc explainability techniques for neural networks, many current methods rely on heuristics and do not provide formally provable guarantees…
What makes an Ensemble (Un) Interpretable?
Shahaf Bassan, Guy Amir, Meirav Zehavi +1
Ensemble models are widely recognized in the ML community for their limited interpretability. For instance, while a single decision tree is considered interpretable, ensembles of t…
CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection
Ron Eliav, Arie Cattan, Eran Hirsch +4
A common approach to hallucination detection casts it as a natural language inference (NLI) task, often using LLMs to classify whether the generated text is entailed by correspondi…
Self-Explaining Neural Networks for Business Process Monitoring
Shahaf Bassan, Shlomit Gur, Sergey Zeltyn +3
Tasks in Predictive Business Process Monitoring (PBPM), such as Next Activity Prediction, focus on generating useful business predictions from historical case logs. Recently, Deep…
Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons
Shahaf Bassan, Ron Eliav, Shlomit Gur
*Minimal sufficient reasons* represent a prevalent form of explanation - the smallest subset of input features which, when held constant at their corresponding values, ensure that…