20 citations · 75 across the 17 of their papers we have counts for
4 papers · 1 filter
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes
Yishay Mansour, Michal Moshkovitz, Cynthia Rudin
Interpretability is an essential building block for trustworthiness in reinforcement learning systems. However, interpretability might come at the cost of deteriorated performance,…
XAudit : A Theoretical Look at Auditing with Explanations
Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
Responsible use of machine learning requires models to be audited for undesirable properties. While a body of work has proposed using explanations for auditing, how to do so and wh…
Finding Safe Zones of policies Markov Decision Processes
Lee Cohen, Yishay Mansour, Michal Moshkovitz
Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset. The quality of a S…
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…