Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning

TL;DR

This collection presents the latest research and discussions from the 2017 NIPS Symposium focused on advancing interpretability in machine learning models.

Contribution

It compiles diverse approaches and insights into making machine learning models more transparent and understandable.

Findings

Various interpretability techniques discussed

New methods for model explanation proposed

Consensus on importance of interpretability in ML

Abstract

This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017