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Showing 2016 · cs.LGShow all
2 papers · 2 filters
cs.LG2016
Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions
Flavian Vasile, Damien Lefortier, Olivier Chapelle
One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions. An unaddressed…
cs.LG2016★ 1 cited
Poor starting points in machine learning
Mark Tygert
Poor (even random) starting points for learning/training/optimization are common in machine learning. In many settings, the method of Robbins and Monro (online stochastic gradient…