Parks, McKenzie Brooke2020-08-282019-052019-10-252019Parks, McKenzie Brooke. 2019. Learning Strategies for Bidding in Online Advertisement Auctions With Noisy Feedback. Bachelor's thesis, Harvard College.https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364601We focus on analyzing the best robust online learning algorithms for bidding in repeated Online Advertisement Auctions. The structure of these auctions creates a unique setting where a bidder only receive information on her value if she wins the auction at a specific time step and then she also observes feedback on other bids she could have submitted. We analyze two strategies that can be employed by a bidder in order to maximize her reward while simultaneously learning her value as the auction progresses. First, we consider an online learner using a Multi-Arm Bandit online learning approach. Second, we leverage the feedback structure of Online Advertisement Auctions to provide a theoretical proof that a noisy variant of a partial feedback online learning algorithm (WIN-EXP \cite{winexp}) has regret rates against the best bid in hindsight that converge quickly, grow only logarithmically with the number of actions a learner can take, and scale proportionally to the square root of the magnitude of the noise. Next, we use an experimental setting to exhibit the robustness of the noisy WIN-EXP algorithm and confirm the theoretical results. Finally, we include auction simulations to demonstrate a trade-off between using an online learning method that may incur some bias in the updates (WIN-EXP) versus using a method that is completely clean (Multi-Armed Bandit EXP3), but may not have the best regret outcomes.application/pdfenThesis or Dissertation2020-08-28