BEAMES: Interactive Multimodel Steering, Selection, and Inspection for Regression Tasks
By Subhajit Das, Dylan Cashman, Remco Chang, A. Endert. 2019
45 citations recorded by the source corpus.
Interactive model steering helps people incrementally build machine learning models that are tailored to their domain and task. Existing visual analytic tools allow people to steer a single model (e.g., assignment attribute weights used by a dimension reduction model). However, the choice of model is critical in such situations. What if the model chosen is suboptimal for the task, dataset, or question being asked? What if instead of parameterizing and steering this model, a different model provides a better fit? This paper presents a technique to allow users to inspect and steer multiple machine learning models. The technique steers and samples models from a broader set of learning algorithms and model types. We incorporate this technique into a visual analytic prototype, BEAMES, that allows users to perform regression tasks via multimodel steering. This paper demonstrates the effectiveness of BEAMES via a use case, and discusses broader implications for multimodel steering.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Cynthia Rudin
- Christian Bauckhage
- Rajkumar Ramamurthy
- Jochen Garcke
In Computer Vision and Pattern Recognition
- Zhi Chen
- Lesia Semenova
- Chudi Zhong
- Haiyang Huang
- Christian Bauckhage
- Jochen Garcke
- Cynthia Rudin
- Bernt Schiele
In Human-Computer Interaction
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “[75] design a system called BEAMES that allows users to interactively”
- Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “Human Feedback [144], [145], [146] [144], [146] [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [157]”