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Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017
Analysis of a Design Pattern for Teaching with Features and Labels
We analyze the potential risks and benefits of this teaching pattern through the use of teaching protocols, illustrative examples, and by providing bounds on the effort required for an optimal machine teacher using a linear learning algorithm, the most commonly used type of learners in interactive machine learning systems.
Interactive Semantic Featuring for Text Classification
In text classification, dictionaries can be used to define human-comprehensible features.
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Based on the machine's output, the teacher can revise the definition of the task or make it more precise.
Counterfactual Reasoning and Learning Systems
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system.
