17 citations · 51 across the 6 of their papers we have counts for
7 papers
Framework for Evaluating Faithfulness of Local Explanations
Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz
We study the faithfulness of an explanation system to the underlying prediction model. We show that this can be captured by two properties, consistency and sufficiency, and introdu…
Computing the Shapley Value of Facts in Query Answering
Daniel Deutch, Nave Frost, Benny Kimelfeld +1
The Shapley value is a game-theoretic notion for wealth distribution that is nowadays extensively used to explain complex data-intensive computation, for instance, in network analy…
Explaining Natural Language Query Results
Daniel Deutch, Nave Frost, Amir Gilad
Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries. We build upon this work, but focus on presenting a highly detailed form…
Just in Time: Personal Temporal Insights for Altering Model Decisions
Naama Boer, Daniel Deutch, Nave Frost +1
The interpretability of complex Machine Learning models is coming to be a critical social concern, as they are increasingly used in human-related decision-making processes such as…
T-REx: Table Repair Explanations
Daniel Deutch, Nave Frost, Amir Gilad +1
Data repair is a common and crucial step in many frameworks today, as applications may use data from different sources and of different levels of credibility. Thus, this step has b…
ExKMC: Expanding Explainable -Means Clustering
Nave Frost, Michal Moshkovitz, Cyrus Rashtchian
Despite the popularity of explainable AI, there is limited work on effective methods for unsupervised learning. We study algorithms for -means clustering, focusing on a trade-of…