The RAIN seminar is held on Wednesdays from 12:00-1:00pm in Y2E2 101 . And yes, lunch is provided!
RAIN schedule for Autumn Quarter 2016-17
||Philippe Rigollet at OIT seminar, room M109 at GSB||
Online Learning in Repeated Auctions
Google Calendar for RAIN
Previous year's talks
Archived talks can be accessed here.
Talk AbstractsOnline Learning in Repeated Auctions
Philippe Rigollet, MIT
Motivated by online advertising auctions, we consider repeated Vickrey auctions where goods of
unknown value are sold sequentially and bidders only learn (potentially noisy) information about
a good's value once it is purchased. We adopt an online learning approach with bandit feedback
to model this problem and derive bidding strategies for two models: stochastic and adversarial.
In the stochastic model, the observed values of the goods are random variables centered around
the true value of the good. In this case, logarithmic regret is achievable when competing against
well behaved adversaries. In the adversarial model, the goods need not be identical. Comparing
our performance against that of the best fixed bid in hindsight, we show that sublinear regret is
also achievable in this case. For both the stochastic and adversarial models, we prove matching
minimax lower bounds showing our strategies to be optimal up to lower-order terms. To our
knowledge, this is the first complete set of strategies for bidders participating in auctions of this
Joint with Jonathan Weed, and Vianney Perchet.
Bio: Philippe Rigollet