Spotlight
in
Workshop: Transparent and interpretable Machine Learning in Safety Critical Environments

Poster spotlights

Hiroshi Kuwajima ⋅ Masayuki Tanaka ⋅ Qingkai Liang ⋅ Matthieu Komorowski ⋅ Fanyu Que ⋅ Thalita F Drumond ⋅ Aniruddh Raghu ⋅ Leo Anthony Celi ⋅ Christina Göpfert ⋅ Andrew Ross ⋅ Sarah Tan ⋅ Rich Caruana ⋅ Yin Lou ⋅ Devinder Kumar ⋅ Graham Taylor ⋅ Forough Poursabzi-Sangdeh ⋅ Jennifer Wortman Vaughan ⋅ Hanna Wallach

2017 Spotlight
in
Workshop: Transparent and interpretable Machine Learning in Safety Critical Environments

Abstract

[1] "Network Analysis for Explanation" [2] "Using prototypes to improve convolutional networks interpretability" [3] "Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning" [4] "Deep Reinforcement Learning for Sepsis Treatment" [5] "Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals" [6] "The Neural LASSO: Local Linear Sparsity for Interpretable Explanations" [7] "Detecting Bias in Black-Box Models Using Transparent Model Distillation" [8] "Data masking for privacy-sensitive learning" [9] "CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy" [10] "Manipulating and Measuring Model Interpretability"

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