Domain Adaptation in Regression

By Corinna Cortes, Mehryar Mohri. 2011

129 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cancer Research, Representation & Self-Supervised Learning

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Citations in the corpus

Unsupervised Domain Adaptation by Backpropagation (2014, background): “A saddle point (2)-(3) can be found as a stationary point of the following stochastic updates: where µ is the learning rate (which can vary over time).”
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data (2020, background): “It was shown by Mansour et al. (2009c) (see also Cortes and Mohri (2011)) that the discrepancy is the appropriate divergence between distributions in adaptation.”
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment (2019, background): “Existing theories without assumptions of contained support (Ben-David et al., 2007, 2010a; Ben-David & Urner, 2014; Mansour et al., 2009; Cortes & Mohri, 2011) do not exhibit this property since (i) when applied to the input space, their results are not concerned with domain-adversarial learning as…”
Causal Transfer in Machine Learning (2015, methodology): “ product (star rating from 1 to 5). In our experiments, each of these four groups is considered a task. Our goal is to predict a product’s rating given the review text. We extract features following [Cortes and Mohri, 2011]: we count the 1000 top words in all tasks and use the normalised counts as f”
A Survey on Negative Transfer (2020, background): “[71] derived a theoretical bound on the test error and proposed a Bayesianoptimization based approach to estimate this bound to guarantee positive transfer in robot trajectory tracking.”
New Analysis and Algorithm for Learning with Drifting Distributions (2012, background): “However, in some cases, for example for kernel-based hypothesis sets based on a Gaussian kernel, the discrepancy has been shown to be a distance [20].”
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning (2016, methodology): “Corollary 12.”
Domain adaptation and sample bias correction theory and algorithm for regression (2014, background): “1 This is an extended version of the conference paper [8] including more details and additional theoretical and empirical results.”
Support and Invertibility in Domain-Invariant Representations (2019, methodology): “Similar results have also been obtained for continuous labels (Mansour et al. , 2009; Cortes & Mohri, 2011).”
Domain Aggregation Networks for Multi-Source Domain Adaptation (2019, background): “…then disc( (cid:98) T, (cid:98) S i ) = (cid:107) M T − M S i (cid:107) 2 is the largest eigenvalue (in magnitude) of the difference of two matrices (Mansour et al., 2009a; Cortes & Mohri, 2011, Sec.5), which can be conveniently approximated using mini-batches and a few steps of power iteration.”
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…”
Adaptation Based on Generalized Discrepancy (2019, methodology): “2 Pointwise Guarantees Similar to the guarantee presented by Cortes and Mohri (2013), we will seek to bound the difference between an ideal solution h∗ and the solution obtained by our algorithm.”