Online Bidding under RoS Constraints without Knowing the Value

This introduces a Online advertising, a multi-billion dollar industry, relies on realtime challenging exploration-exploitation dilemma: the advertiser must auctions to connect advertisers with users. These auctions, balance exploring different bids to estimate impression values with triggered by user queries or website visits, allow advertisers to exploiting current knowledge to bid effectively. To address this, we bid for advertising slots, such as prominent placements on search propose a novel Upper Confidence Bound (UCB)-style algorithm engine results pages or in social media feeds. Advertisers aim to that carefully manages this trade-off. Via a rigorous theoretical maximize their returns, measured in conversions or other relevant analysis, we prove that our algorithm achieves ( log(|B|)) metrics, by carefully determining their bids while adhering to regret and constraint violation, where is the number of bidding budget constraints and desired return-on-spend (RoS) targets. To rounds and B is the domain of possible bids. This establishes the achieve this, a wide array of bidding strategies have been developed, first optimal regret and constraint violation bounds for bidding in leveraging techniques from optimization, online learning, and game the online setting with unknown impression values. Moreover, our theory to maximize advertiser utility [2, 7, 11, 23, 29, 30, 36, 47, 52].