Mixed-Initiative Active Learning
By Maya Cakmak, Andrea Lockerd Thomaz. 2012
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Ji Liu
- Xuezhou Zhang
- Robert D. Nowak
- Xiaojin Zhu
- Kristian Kersting
- Philippe Rigollet
- H. Ohannessian
- Stefano Teso
Citations in the corpus
- The Teaching Dimension of Linear Learners (2015, background): “Teaching dimension also has applications in interactive machine learning to quantify the minimum human interaction necessary [10], and in formal synthesis to generate computer programs satisfying a specification [15].”
- Explanatory Interactive Machine Learning (2019, background): “teractive learning can be made explanatory too, including coactive learning [33], active imitation learning [15], and mixed-initiative interactive learning [5], but this is beyond the scope of this paper.”
- Teacher Improves Learning by Selecting a Training Subset (2018, background): “Machine teaching applications include education [14, 33, 39, 27, 13, 34], computer security [2, 1, 32], and interactive machine learning [40, 12, 24].”