interpretable machine learning

Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health IJCAI 2020 MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning AAAI 2020 ProtoAttend: Attention-Based Prototypical Learning JMLR 2020 Rationalizing Medical Relation Prediction from Corpus-level Statistics ACL 2020 Regularizing Black-box Models for Improved Interpretability NeurIPS 2020 Synthesizing Action Sequences for Modifying Model Decisions AAAI 2020 Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs ACL 2020 Transparent Classification with Multilayer Logical Perceptrons and Random Binarization AAAI 2020 Turning 30: New Ideas in Inductive Logic Programming IJCAI 2020 Where to Submit? Helping Researchers to Choose the Right Venue EMNLP 2020 Abduction-Based Explanations for Machine Learning Models AAAI 2019 Interpretable Cascade Classifiers with Abstention AISTATS 2019 Interpretable Question Answering on Knowledge Bases and Text ACL 2019 Optimal Sparse Decision Trees NeurIPS 2019 Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control EMNLP 2019 The Role of Textualisation and Argumentation in Understanding the Machine Learning Process IJCAI 2017 RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism NeurIPS 2016