Learning and evaluating classifiers under sample selection bias

By Bianca Zadrozny. 2004

921 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Statistical Learning Theory

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Citations in the corpus

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).”
Multi-Source Domain Adaptation: A Causal View (2015, background): “It is also known as sample selection bias (particularly on the features X ) in (Zadrozny 2004).”
Balanced Linear Contextual Bandits (2018, methodology): “2007), (Zadrozny 2004), (Cortes, Mansour, and Mohri 2010).”
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].”
Pre-Trained Models: Past, Present and Future (2021, background): “…transfer learning (Lawrence and Platt, 2004; Mihalkova et al., 2007; Evgeniou and Pontil, 2004), transductive transfer learning (Shimodaira, 2000; Zadrozny, 2004; Daume III and Marcu, 2006), self-taught learning (Raina et al., 2007; Dai et al., 2008), 4 and unsupervised transfer learning (Wang…”
Lifelong Learning for Sentiment Classification (2015, background): “The key reason is that the training data may not be fully representative of the test data due to the sample selection bias (Heckman, 1979, Shi-modaira, 2000, Zadrozny, 2004).”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, methodology): “This is done either by estimating these probabilities independently with a domain classifier [267] or by”
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation (2016, background): “Domain adaptation is a large ﬁeld of research, with related work under several names such as class imbalance [20], covariate shift [21], and sample selection bias [22].”
Residual Unfairness in Fair Machine Learning from Prejudiced Data (2018, background): “The case of sample selection bias was studied in Zadrozny (2004) for classiﬁer evaluation, without regard for fairness impacts.”
Fairness without Demographics through Adversarially Reweighted Learning (2020, background): “Domain adaptation work requires a model to be robust and generalizable across different domains, under either covariate shift [53, 48] or label shift [40].”
Topic-bridged PLSA for cross-domain text classification (2008, methodology): “Zadrozny [24] proposed a two-step approach for correcting sample selection.”
Fairness-Aware Classifier with Prejudice Remover Regularizer (2012, background): “It is known that the problem of a sample selection bias can be avoided by adopting specific types of classification algorithms [24].”
A Literature Survey on Domain Adaptation of Statistical Classifiers (2007, background): “In some other work, it is proposed to transform this density ratio estimation into a problem of predicting whether an instance is from the source domain or from the target domain (Zadrozny, 2004; Bickel and Scheffer, 2007).”
The art of modelling range‐shifting species (2010, background): “For most models, predictions to areas with substantially different correlations between important variables will be unreliable (Harrell 2001; but see Zadrozny 2004).”
Small Sample Learning in Big Data Era (2018, methodology): “The early works proposed in this manner were constructed based on instance re-weighting (Zadrozny, 2004; Sugiyama et al., 2008; Kanamori et al., 2009; Huang et al., 2007; Yan et al., 2017), which estimated the ratio between the likelihoods of being a source or target example or use maximum mean…”
A Survey on Negative Transfer (2020, methodology): “ii) Instance level transferability enhancement: Instance selection and instance weighting are frequently used in TL [2], [3], [27].”