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talks:counterfactuals [2013/05/19 15:08]
leonb [Links] 		talks:counterfactuals [2014/12/18 22:16] (current)
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 ====== Counterfactual Reasoning and Learning Systems ====== ====== Counterfactual Reasoning and Learning Systems ======
  
-Variants of this talk were given in several occasions. The slides below were presented at the [[http://research.microsoft.com/en-us/um/cambridge/events/mls2013/|Machine Learning Summit 2013]]. See also lectures 9 and 11 in the NYU course on [[http://cilvr.cs.nyu.edu/doku.php?id=courses:bigdata:slides:start|Big Data and Machine Learning]].+Variants of this talk were given in several occasions,  
 +including the [[http://research.microsoft.com/en-us/um/cambridge/events/mls2013/|Machine Learning Summit 2013]], 
 +the [[http://tce.technion.ac.il/conference/tce-conference-2013/|TCE 2013 Conference]], and 
 +the [[http://shad.yandex.ru/conference/program.xml|Yandex School of Data Analysis 2013 Conference]].  
 +See also lectures 9 and 11 in the 2013 NYU course on [[http://cilvr.cs.nyu.edu/doku.php?id=courses:bigdata:slides:start|Big Data and Machine Learning]].
  
  
 ===== Summary ===== ===== Summary =====
  
 +{{ads.png?250 }}
 Statistical machine learning technologies in the real world are never without a purpose. Using their predictions, humans or machines make decisions whose circuitous consequences often violate the modeling assumptions that justified the system design in the first place. Such contradictions appear very clearly in computational advertisement systems. The design of the ad placement engine directly influences the occurrence of clicks and the corresponding advertiser payments. It also has important indirect effects : (a) ad placement decisions impact the satisfaction of the users and therefore their willingness to frequent this web site in the future, (b) ad placement decisions impact the return on investment observed by the advertisers and therefore their future bids, and (c) ad placement decisions change the nature of the data collected for training the statistical models in the future. Statistical machine learning technologies in the real world are never without a purpose. Using their predictions, humans or machines make decisions whose circuitous consequences often violate the modeling assumptions that justified the system design in the first place. Such contradictions appear very clearly in computational advertisement systems. The design of the ad placement engine directly influences the occurrence of clicks and the corresponding advertiser payments. It also has important indirect effects : (a) ad placement decisions impact the satisfaction of the users and therefore their willingness to frequent this web site in the future, (b) ad placement decisions impact the return on investment observed by the advertisers and therefore their future bids, and (c) ad placement decisions change the nature of the data collected for training the statistical models in the future.
  
talks/counterfactuals.1368990501.txt.gz · Last modified: 2013/05/19 15:08 by leonb

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