Previous ad auctions predominantly relied on rule-based mechanisms, which selected winning advertisements (ads) at the ad-level and subsequently combined them into page views (PVs), leading to suboptimal allocations in multi-round auctions. This limitation stems from the significant computational burden required to design ranking score rules and select winning ad sets, as well as the inability to fully capture contextual information within PVs during ad-level selection. In this paper, we propose a key-performance-indicator (KPI) based auction mechanism that selects winning PVs at the PV-level, modeling the ad allocation as a constrained optimization problem. This approach enables us to address both short-term and long-term KPIs while leveraging the comprehensive contextual information available within PVs. Based on this framework, we design GenAuction, a generative auction mechanism utilizing a Generator-Evaluator architecture powered by Transformer algorithms. The Generator swiftly generates multiple candidate PVs, while the Evaluator selects the optimal PVs based on contextual information, adhering to the objectives and KPIs of multi-round auctions. We conduct extensive experiments using real-world data and online A/B tests to validate that GenAuction efficiently handles multi-objective allocation tasks, demonstrating its efficacy and potential for real-world application.

In many social systems in which individuals and organizations interact with each other, there can be no easy laws to govern the rules of the environment, and agents' payoffs are often influenced by other agents' actions. We examine such a social system in the setting of sponsored search auctions and tackle the search engine's dynamic pricing problem by combining the tools from both mechanism design and the AI domain. In this setting, the environment not only changes over time, but also behaves strategically. Over repeated interactions with bidders, the search engine can dynamically change the reserve prices and determine the optimal strategy that maximizes the profit. We first train a buyer behavior model, with a real bidding data set from a major search engine, that predicts bids given information disclosed by the search engine and the bidders' performance data from previous rounds. We then formulate the dynamic pricing problem as an MDP and apply a reinforcement-based algorithm that optimizes reserve prices over time. Experiments demonstrate that our model outperforms static optimization strategies including the ones that are currently in use as well as several other dynamic ones.

The credibility of digital images is decreasing with the fast development of easy-to-use image manipulation tools such as Photoshop and Picasso. Although good forged images are deceptive to human eyes, the manipulation process often leaves some statistical traces in the forged images. Very often, the forged images are rotated partly or as a whole. If we can identify image rotation or even determine image rotation angle, the authenticity of images could be verified. In this paper, we propose an image rotation angle estimation algorithm based on properties of 2-D DFT of pixel variance. This algorithm is a blind one because it works in absence of any prior information such as digital watermark. We show the efficacy of this approach in our experiment.