  In markets for online advertising, some advertisers pay only when users
respond to ads. So publishers estimate ad response rates and multiply by
advertiser bids to estimate expected revenue for showing ads. Since these
estimates may be inaccurate, the publisher risks not selecting the ad for each
ad call that would maximize revenue. The variance of revenue can be decomposed
into two components -- variance due to `uncertainty' because the true response
rate is unknown, and variance due to `randomness' because realized response
statistics fluctuate around the true response rate. Over a sequence of many ad
calls, the variance due to randomness nearly vanishes due to the law of large
numbers. However, the variance due to uncertainty doesn't diminish.
  We introduce a technique for ad selection that augments existing estimation
and explore-exploit methods. The technique uses methods from portfolio
optimization to produce a distribution over ads rather than selecting the
single ad that maximizes estimated expected revenue. Over a sequence of similar
ad calls, ads are selected according to the distribution. This approach
decreases the effects of uncertainty and increases revenue.
