Accès ouvert
2026
article
OpenAlex
David Martens, Galit Shmueli, Theodoros Evgeniou, Kevin Bauer et autres
Abstract Understanding the decisions made and actions taken by increasingly complex AI systems remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and …
be, tw, fr, de, nl, dk, us
(code pays fourni par la source)
2023
conference-paper
OpenAlex
Gianmarco De Francisci Morales, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis et autres
us, es, it
(code pays fourni par la source)
2023
conference-paper
OpenAlex
Gianmarco De Francisci Morales, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis et autres
us, es, it
(code pays fourni par la source)
2019
dataset
OpenAlex
Claudia Perlich
2018
conference-paper
OpenAlex
Yeming Shi, Claudia Perlich, Rod Hook, Wickus Martin et autres
A growing proportion of digital advertising slots is purchased through real time bidding auctions, which enables advertisers to impose highly specific criteria on which devices and opportunities to target. Employing sophisticated targeting criteria reliably increases the performance of an ad campaign, however …
2017
conference-paper
OpenAlex
Yeming Shi, Ori Stitelman, Claudia Perlich
Every day, billions of online advertising slots are bought and sold through real time bidding (RTB). In RTB, publishers sometimes reject bids to deliver ads (impressions) for some brands, due to, for example, direct deals with other brands. Publishers rarely disclose which …
2017
reference-entry
OpenAlex
Claudia Perlich
us
(code pays fourni par la source)
2016
conference-abstract
OpenAlex
Claudia Perlich
Machine Learning research is progressing at an ever increasing pace. Fueled by technology advances commonly referred to as "Big Data", all data related fields are teaming with scientific and applied activity: our communities explore new application areas, develop new learning algorithms, and …
Accès ouvert
2015
article
OpenAlex
B D'Alessandro, Rod Hook, Claudia Perlich, Foster Provost
Online systems promise to improve advertisement targeting via the massive and detailed data available. However, there often is too few data on exactly the outcome of interest, such as purchases, for accurate campaign evaluation and optimization (due to low conversion rates, cold …
us
(code pays fourni par la source)
2014
conference-paper
OpenAlex
Melinda Han Williams, Claudia Perlich, B D'Alessandro, Foster Provost
Most video advertising campaigns today are still evaluated based on aggregate demographic audience metrics, rather than measures of individual impact or even individual demographic reach. To fit in with advertisers' evaluations, campaigns must be optimized toward validation by third-party measurement companies, which …
us
(code pays fourni par la source)
2014
conference-paper
OpenAlex
B D'Alessandro, Daizhuo Chen, Troy Raeder, Claudia Perlich et autres
Internet display advertising is a critical revenue source for publishers and online content providers, and is supported by massive amounts of user and publisher data. Targeting display ads can be improved substantially with machine learning methods, but building many models on massive …
Accès ouvert
2014
article
OpenAlex
Foster Provost, Geoffrey I. Webb, Ron Bekkerman, Oren Etzioni et autres
In August 2013, we held a panel discussion at the KDD 2013 conference in Chicago on the subject of data science, data scientists, and start-ups. KDD is the premier conference on data science research and practice. The panel discussed the pros and …
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