Domain adaptation and sample bias correction theory and algorithm for regression

By Corinna Cortes, Mehryar Mohri. 2014

218 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Representation & Self-Supervised Learning

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “Given a global model that performs reasonably well, what is the best way to personalize it? In nonfederated learning, researchers often use fine-tuning, transfer learning, domain adaptation [284, 115, 56], or interpolation with a personal local model.”
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].”
Agnostic Federated Learning (2019, background): “…adaptation from a single source to a known target domain (Ben-David, Blitzer, Crammer, and Pereira, 2006; Mansour, Mohri, and Rostamizadeh, 2009b; Cortes and Mohri, 2014; Cortes, Mohri, and Mu˜noz Medina, 2015), either through unsupervised adaptation techniques (Gong et al., 2012; Long et al.,…”
Algorithms and Theory for Multiple-Source Adaptation (2018, background): “The problem of transfer from a single source to a known target domain (Ben-David, Blitzer, Crammer, and Pereira, 2006; Mansour, Mohri, and Rostamizadeh, 2009b; Cortes and Mohri, 2014; Cortes, Mohri, and Muñoz Medina, 2015), either through unsupervised adaptation techniques (Gong et al.”
Domain-Adversarial Neural Networks (2014, background): “A large part of the literature however has focused mainly on linear hypothesis (see for instance Blitzer et al., 2006; Bruzzone & Marconcini, 2010; Germain et al., 2013; Baktashmot-lagh et al., 2013; Cortes & Mohri, 2014).”
Theoretical Analysis of Domain Adaptation with Optimal Transport (2016, background): “On the other hand, [15] and [5] proposed to replace it with the discrepancy distance:”
An introduction to domain adaptation and transfer learning (2018, background): “But this tends to have a negative effect on the variance of the importance weights [46,44,45].”
Bounding and Minimizing Counterfactual Error (2016, result): “This is in contrast to [20] who proposed a two-step procedure corresponding to their theoretical results based on the discrepancy distance [7].”
Time series prediction and online learning (2016, methodology): “…(Cortes and Mohri, 2014) or by writing it as an equivalent SDP problem which can then be solved in polynomial-time using interior-point methods and several other specific algorithms (Fletcher, 1985; Overton, 1988; Jarre, 1993; Helmberg and Oustry, 2000; Alizadeh, 1995; Cortes and Mohri, 2014).”
A Review of Domain Adaptation without Target Labels (2019, methodology): “weighting [6], [7] or class importance-weighting [8].”
Domain-Adversarial Training of Neural Networks (2015, background): “Over the years, a large part of the literature has focused mainly on linear hypothesis (see for instance Blitzer et al., 2006; Bruzzone and Marconcini, 2010; Germain et al., 2013; Baktashmotlagh et al., 2013; Cortes and Mohri, 2014).”
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning (2016, background): “Also, in the context of domain adaptation, similar considerations are examined by [41, 43, 42, 21, 66, 44] and [22].”
Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, background): “In regression, Cortes and Mohri [42] showed that the discrepancy between domains is a distance for the squared loss when the hypothesis set is the reproducing kernel Hilbert space induced by an universal kernel.”
On Localized Discrepancy for Domain Adaptation (2020, background): “After that, Cortes and Mohri (2014) proposes detailed studies on domain adaptation in regression tasks.”
Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process (2021, methodology): “(14), was used to derive generalization bounds for domain adaptation methods [3, 7, 26] and also for matching generated and real data distributions in the GAN’s discriminator during training [9, 21, 22].”
Adaptation Algorithm and Theory Based on Generalized Discrepancy (2014, methodology): “Several generalization bounds for adaptation in terms of the discrepancy have been given in the past (Ben-David et al., 2006; Mansour et al., 2009; Cortes and Mohri, 2011, 2013), including pointwise guarantees in the case of kernel-based regularization algorithms, which includes algorithms such as…”