Interactive Machine Learning Mustafa Bilgic

By Maria-Florina Balcan. 2000

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Citations in the corpus

The Principles and Limits of Algorithm-in-the-Loop Decision Making (2019, background): “In the context of algorithm-assisted human decision making, prior research has explored topics such as what interactions can facilitate the development of machine learning models [9, 29, 47], how to improve human performance with an algorithm’s assistance [12, 37, 48], and the ways in which laypeopl”
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts (2021, methodology): “State-of-the-art systems – enable the user to perform corrections on labels and to actCAIPI [129] upon wrong explanations through interactive machine learning (intML), such as implemented in the CAIPI approach [129], – they allow for re-weighting of features for explanatory debug-EluciDebug [81] gin”
Learning an interactive segmentation system (2009, methodology): “Interactive image classification without user model is discussed in the machine learning literature [10] and active inference in random field graphs is already used in the knowledge discovery community [4].”
Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making (2019, unknown): “Interactive ML incorporates human feedback in the model training process to create better ML models [16], and has been an active area of research [4, 18, 28, 40].”
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020, background): “[18] to describe an approach where people are involved in an iterative process of training, using, and correcting an ML model, requiring interpretability for effective corrections [13].”
Power to the People: The Role of Humans in Interactive Machine Learning (2014, background): “…and refinement. research, however, has started to explore new interfaces, including interfaces for letting users label parts of items (e.g., Fails & Olsen 2003), adjust model parameters and cost functions (e.g., Kapoor et al. 2010), and directly modify a classifier’s features (e.g.,…”