Correcting Sample Selection Bias by Unlabeled Data

By Jiayuan Huang, Alex Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf. 2006

1,918 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Representation & Self-Supervised Learning

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Citations in the corpus

Domain-Adversarial Training of Neural Networks (2015, background): “Some ap-3 proaches perform this by reweighing or selecting samples from the source domain (Borg-wardt et al., 2006; Huang et al., 2006; Gong et al., 2013), while others seek an explicit feature space transformation that would map source distribution into the target one (Pan et al., 2011; Gopalan et…”
A theory of learning from different domains (2010, background): “A well-studied variant of this is covariate shift, which has seen signiﬁcant work in recent years (Huang et al. 2007; Sugiyama et al. 2008; Cortes et al. 2008).”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “…shift (Gretton et al. 2009, Pan et al. 2011) and domain adaptation (Blanchard et al. 2011, Muandet et al. 2013), selection bias correction (Huang et al. 2007), herding (Chen et al. 2010, Huszar and Duvenaud 2012), Markov chain Monte Carlo (Sejdinovic et al. 2014), moment matching for…”
Marginalized Denoising Autoencoders for Domain Adaptation (2012, methodology): “Domain adaptation (Ben-David et al., 2009; Huang et al., 2007; Weinberger et al., 2009; Xue et al., 2008) aims to generalize a classiﬁer that is trained on a source domain, for which typically plenty of training data is available, to a target domain, for which data is scarce.”
Algorithms and Theory for Multiple-Source Adaptation (2018, positive): “We also compared against a previously proposed domain adaptation algorithm (Huang et al., 2006) known as KMM. It is important to note that the KMM model requires access to the unlabeled target data during adaptation and learns a new predictor for every target domain, while DW does not use any target”
Recommendations as Treatments: Debiasing Learning and Evaluation (2016, methodology): “Weighting approaches are also widely used in domain adaptation and co-variate shift, where data from one source is used to train for a different problem (e.g., Huang et al., 2006; Bickel et al., 2009; Sugiyama & Kawanabe, 2012).”
Domain Generalization via Conditional Invariant Representations (2018, background): “Domain adaptation is a related problem which has been extensively studied in the literature (Baktashmotlagh et al. 2013; Huang et al. 2007; Pan et al. 2011; Long et al. 2017; Shao, Kit, and Fu 2014; Shao et al. 2016; Luo et al. 2017; Liu, Yang, and Tao 2017; ?”
Detecting and Correcting for Label Shift with Black Box Predictors (2018, background): “Covariate shift, also called sample selection bias , is well-studied (Zadrozny, 2004; Huang et al., 2007; Sugiyama et al., 2008; Gretton et al., 2009).”
Domain Adaptation with Conditional Transferable Components (2016, methodology): “We compare CTC and CTC-TIP with KMM, surrogate kernels (SuK) (Kai Zhang et al., 2013), TCA, DIP and DIP-CC, and GeTarS.”
Multi-Source Domain Adaptation: A Causal View (2015, methodology): “KMM for correcting co-variate shift (Huang et al. 2007), the pooling SVM (denoted pool SVM ), which merge all source data to train the SVM, domain-invariant component analysis (DICA) (Muandet, Bal-duzzi, and Sch¨olkopf 2013), and Learning marginal predictors (LMP) proposed by (Blanchard, Lee, and…”
Counterfactual reasoning and learning systems: the example of computational advertising (2012, background): “The variance of importance sampling schemes has been abundantly studied in the Covariate Shift literature (e.g., Shimodaira, 2000; Huang et al., 2007) and the Markov Chain Monte Carlo literature (e.”
Estimation Considerations in Contextual Bandits (2017, methodology): “The balancing technique is well-known in machine learning, especially in domain adaptation and studies in learning-theoretic frameworks [35, 65, 24].”
Learngene: From Open-World to Your Learning Task (2021, background): “Transfer learning: Methods such as (Huang et al. 2006; Sugiyama et al. 2008; Sun et al. 2011; Shen et al. 2018; Lee et al. 2019; Duan et al. 2009; Zhuang et al. 2009; Evgeniou, Micchelli, and Pontil 2005), which focus on transferring the knowledge across domains, aim at improving the performance of ”
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment (2019, background): “, 2018) or Maximum Mean Discrepancy (Huang et al., 2007) have the property that D(pφS , p φ T ) = 0 if p φ S ≡ p φ T and D(pφS , p φ T ) > 0 otherwise.”
A Comprehensive Survey on Transfer Learning (2019, methodology): “A number of transfer learning approaches, for example, FAM [64] and the variant of KMM [5], are compared with each other.”