In the field of auction design, leveraging deep learning to solve optimal auctions from sampled data has become a promising direction. However, real-world contexts often involve uncertain data, which would severely affect the auction performance, but it is lacking consideration in existing works. To address this challenge, we incorporate these uncertainties into auction design metrics, and frame this challenge as a robust data-driven auction design problem. To solve this problem, we first propose the GAT method, where we introduce the process of problem relaxation and transformation to address the non-differentiable variable presented in the original problem, and further propose an adversarial training algorithm to solve the mini-max problem after transformation. Moreover, to obtain moderately robust auctions, we propose two methods to select the robust coefficient, which provides guidance and insights for selecting robust auctions based on generalization and performance metrics. Finally, with the insights from the GAT method, we further propose the SAT method, where we employ a strict and unified IC constraint that extends from the GAT method, which provides strong IC guarantees and stable revenue in uncertain environments. Experiments on both constructed and real-world datasets show that our robust methods effectively improve the performance of auctions in terms of revenue and IC guarantees.

The rise of social bots poses a significant threat to online platforms, making their detection an urgent priority.Recent advancements in Graph Neural Networks (GNNs) have significantly improved bot detection by leveraging the rich relational data within social networks.However, existing approaches face two key challenges: imbalanced feature fusion across different modalities and edge heterophily, which limit their effectiveness.To address these issues, we propose BotBR, a novel bot detection framework.BotBR tackles feature imbalance by employing decision trees to extract behavioral patterns from user-related numerical features and utilizes an attention mechanism to seamlessly integrate multi-modal feature embeddings.To mitigate edge heterophily, BotBR incorporates an edge detector to differentiate between high-and low-reliability edges, optimizing the utilization of structural graph information.Furthermore, a homophily-based graph is introduced for consistency contrastive learning, enhancing the model's robustness.Experimental results on real-world bot detection benchmark datasets demonstrate that BotBR achieves state-of-the-art performance while maintaining efficiency comparable to classical methods.

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 bid. However, the winning price information available to the Demand Side Platforms (DSPs) is extremely limited, and the associated uncertainties make it challenging for DSPs to accurately estimate winning price distribution. To address this challenge, we conducted a comprehensive analysis of the process by which DSPs obtain winning price information, and abstracted two types of uncertainties from it: known uncertainty and unknown uncertainty. Based on these uncertainties, we proposed two levels of robust bidding strategies: Robust Bidding for Censorship (RBC) and Robust Bidding for Distribution Shift (RBDS), which offer guarantees for the surplus in the worst-case scenarios under uncertain conditions. Experimental results on public datasets demonstrate that our robust bidding strategies consistently enable DSPs to achieve superior surpluses, both on test sets and under worst-case conditions.