Semi-supervised multi-task learning of structured prediction models for web information extraction

By Paramveer S. Dhillon, Sundararajan Sellamanickam, S. Sathiya Keerthi. 2011

11 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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. Aurélien Bellet
2. Valentina Zantedeschi
3. Marc Tommasi
4. Rachid Guerraoui
5. Mahsa Taziki
6. Edwige Cyffers

In Computer Networks and Communications

1. Marc Tommasi
2. Aurélien Bellet
3. Rachid Guerraoui
4. Mahsa Taziki
5. Valentina Zantedeschi

In Information Systems

1. Aurélien Bellet
2. Marc Tommasi
3. Rachid Guerraoui
4. Valentina Zantedeschi
5. Edwige Cyffers

Citations in the corpus

• Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs (2019, methodology): “This principle, known as graph regularization, is well-established in the multi-task learning literature (Evgeniou and Pontil, 2004; Maurer, 2006; Dhillon et al., 2011).”
• Personalized and Private Peer-to-Peer Machine Learning (2017, methodology): “Following a well-established principle in the multi-task learning literature (Evgeniou and Pontil, 2004; Maurer, 2006; Dhillon et al., 2011), we use graph regularization to favor models that vary smoothly on the graph.”