Online learning algorithms for bidding in repeated second-price auctions achieve rate-optimal regret by modeling ad value as a causal treatment effect and exploiting second-price payment information.
Proceedings of the 2018 ACM Conference on Economics and Computation , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.
citing papers explorer
-
The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
Online learning algorithms for bidding in repeated second-price auctions achieve rate-optimal regret by modeling ad value as a causal treatment effect and exploiting second-price payment information.
-
Privacy Preserving Reinforcement Learning with One-Sided Feedback
POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.