Artificial intelligence-empowred Real-Time Bidding (AIRTB) is regarded as one of the most
enabling technologies for online advertising. It has attracted significant research attention …

In online advertising, existing auto-bidding strategies for bid shading mainly adopt the
approach of first predicting the winning price distribution and then calculating the optimal …

Online advertising has been one of the most important sources for industry's growth, where
the demand-side platforms (DSP) play an important role via bidding to the ad exchanges on …

We study the optimal bids and allocations in a real-time auction for heterogeneous items
subject to the requirement that specified collections of items of given types be acquired …

Online publishers typically sell ad impressions through auctions held in ad exchanges in
real-time, ie, real-time bidding (RTB). A publisher will accept the winning bid if it is higher …

The increasing demand for online advertising leads to a strong competition in Real-Time
Biddinng (RTB) industry. It requires Demand-Side Platforms (DSPs) to perform a proper …

This thesis studies problems at the intersection of monotone and convex optimization,
auction theory, and electronic commerce. Convex optimization and the theory of stochastic …

Maximizing utility with a budget constraint is the primary goal for advertisers in real-time
bidding (RTB) systems. The policy maximizing the utility is referred to as the optimal bidding …

We propose a general stochastic framework for modelling repeated auctions in the Real
Time Bidding (RTB) ecosystem using point processes. The flexibility of the framework allows …

Real-time bidding has emerged as an effective online advertising technique. With real-time
bidding, advertisers can position ads per impression, enabling them to optimise ad …