Structured labeling for facilitating concept evolution in machine learning

By Todd Kulesza, Saleema Amershi, R. Caruana, Danyel Fisher, Denis Xavier Charles. 2014

125 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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

1. Yuji Roh
2. Ben Green
3. Steven Euijong Whang
4. Geon Heo
5. Yiling Chen
6. Saleema Amershi
7. Ece Kamar
8. Joseph Chee Chang

In Computer Science Applications

1. Saleema Amershi
2. Ece Kamar
3. Geon Heo
4. Yuji Roh
5. Steven Euijong Whang
6. Joseph Chee Chang
7. Ben Green

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “which may evolve as more examples are labeled [117].”
• Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, neutral): “In contrast to the fair ML literature, HCI research on ML developer tools has often focused on the design of user interfaces to scaffold data collection and curation processes (e.g., [20, 22, 36, 62]).”
• The Principles and Limits of Algorithm-in-the-Loop Decision Making (2019, background): “…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 laypeople perceive algorithmic decisions [4,…”
• Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets (2017, methodology): “We obtained our Travel and Gardening webpage datasets from [33] which has approximately 100 webpages for each concept obtained from the Open Directory Project by selecting half from each category, “travel” or “gardening”, and then selecting the remainder randomly from the database.”
• 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].”