76 citations · 115 across the 6 of their papers we have counts for
6 papers
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
Andrew Gordon Wilson, Jason Yosinski, Patrice Simard +2
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017
Machine Teaching: A New Paradigm for Building Machine Learning Systems
Patrice Y. Simard, Saleema Amershi, David M. Chickering +8
The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning…
Analysis of a Design Pattern for Teaching with Features and Labels
Christopher Meek, Patrice Simard, Xiaojin Zhu
We study the task of teaching a machine to classify objects using features and labels. We introduce the Error-Driven-Featuring design pattern for teaching using features and labels…
Interactive Semantic Featuring for Text Classification
Camille Jandot, Patrice Simard, Max Chickering +2
In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. Thes…
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Patrice Simard, David Chickering, Aparna Lakshmiratan +7
Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine to…
Counterfactual Reasoning and Learning Systems
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela +6
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 t…