Adaptation Based on Generalized Discrepancy

By Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina. 2019

82 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mehryar Mohri
2. Corinna Cortes
3. Junfeng Wen
4. Ananda Theertha Suresh
5. Zhenyu Zhang
6. Zhi-Hua Zhou
7. Yoshua Bengio
8. Dale Schuurmans

Citations in the corpus

• Multiple-Source Adaptation with Domain Classifiers (2020, background): “The general theoretical problem of adaptation from a single domain to a target domain has been studied in a series of publications in the last two decades or so [Kifer et al., 2004, Ben-David et al., 2007, Blitzer et al., 2008, Mansour et al., 2009b, Cortes and Mohri, 2014, Cortes et al., 2019b].”
• Learning with Feature and Distribution Evolvable Streams (2020, methodology): “To deal with this difficulty, we resort to the technique of discrepancy minimization (Ben-David et al., 2007; Mansour et al., 2009; Cortes et al., 2019) instead of learning the mapping functions, with the purpose of designing effective algorithms with sound theoretical guarantees.”
• Domain Aggregation Networks for Multi-Source Domain Adaptation (2019, methodology): “This work uses the discrepancy (Mansour et al., 2009a; Cortes et al., 2019) to derive a finite-sample generalization bound for multi-source to single-target adaptation.”