Non-Linear Domain Adaptation with Boosting

38 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.

Few-Shot Adversarial Domain Adaptation (2017, methodology): “Finally, some use regularization to improve the fit on the target domain [4, 1, 62, 10, 3, 8].”
Unified Deep Supervised Domain Adaptation and Generalization (2017, background): “The third one regularizes a classifier trained on a source distribution to work well on a target distribution [4, 1, 66, 15, 3, 12].”
Beyond Sharing Weights for Deep Domain Adaptation (2016, methodology): “This was done, for example, using SVM [11], [12], Boosted Decision Trees [13] and other classifiers [14].”
On Learning Invariant Representations for Domain Adaptation (2019, background): “, 2009a; Mansour & Schain, 2012) and algorithms (Glorot et al., 2011; Becker et al., 2013; Ajakan et al., 2014; Adel et al., 2017; Pei et al., 2018) under this setting are abundant.”
Generalized Dictionary for Multitask Learning with Boosting (2016, background): “There have also been boosting-based multitask learning algorithms proposed in [Chapelle et al., 2010; Becker et al., 2013], but both of these approaches implicitly assume that all the tasks are related to each other, and fail to capture more sophisticated task relatedness such as grouped and/or…”