Machine learning for targeted display advertising: transfer learning in action

By Claudia Perlich, Brian M. D’Alessandro, Troy Raeder, Ori Stitelman, Foster J. Provost. 2013

179 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Wasserstein Fair Classification (2019, background): “…and online advertising (De Fauw et al., 2018; Dieterich et al., 2016; Eubanks, 2018; Hoffman et al., 2018; Malekipir-bazari and Aksakalli, 2015; Perlich et al., 2014), is raising concern that bias in the data and model inaccuracies can lead to decisions that are “unfair” towards…”
A Causal Bayesian Networks Viewpoint on Fairness (2018, neutral): “ion Machinelearningisincreasinglyusedinawiderangeofdecision-makingscenarios that have serious implications for individuals and society, including ﬁnancial lending[10,36],hiring[8,28],onlineadvertising[27,41],pretrialandimmigration detention[5,43],childmaltreatmentscreening[14,47],healthcare[19,32],a”
Fairness in Machine Learning (2020, background): “…scenarios that have serious implications for individuals and society, including ﬁnancial lending [20, 120], hiring [17, 77], online advertising [72, 150], pretrial and immigration detention [10, 158], child maltreatment screening [30, 171], health care [40, 105], social services [4, 48], and…”
Machine learning for science and society (2013, background): “Another perspective comes from Perlich et al. (2013), who discuss that data from outside the exact target domain can be useful.”
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training (2021, background): “Testing for fairness in online learning: When testing an online-learning software system for fairness (where models are continuously updated), such as identity-inference-based advertisement targeting systems [7, 70, 101], multiple identical runs are necessary, and statistical test results should be ”
Fair Algorithms for Clustering (2019, background): “tter in practice than what our theoretical results suggest. 1 Introduction Many important decisions today are made by machine learning algorithms. These range from showing advertisements to customers [59,31], to awarding home loans [47,56], to predicting recidivism [10,32,27]. It is important to ens”
Scalable hands-free transfer learning for online advertising (2014, unknown): “Our current system addresses these issues with a number of di ↵ erent components, amply described in prior work (see [11] and the citations therein).”
Beat the Machine: Challenging Humans to Find a Predictive Model's “Unknown Unknowns” (2015, methodology): “Furthermore, very often, the processes used to generate training sets in real applications are not random sampling at all; they are biased based on pragmatic constraints [Perlich et al. 2014].”
A Survey on Negative Transfer (2020, background): “Finding common and domain-specific latent rating patterns simultaneously [168] can effectively handle this problem.”
Predictive Modeling With Big Data: Is Bigger Really Better? (2013, unknown): “Perlich et al. 15 show increasing returns in generalization performance with increasingly larger data across dozens of datasets; however, although large for empirical machine learning studies at the time, the data sizes are not massive by present standards.”