The field of auction mechanisms and fair allocation is witnessing significant advancements, driven by the integration of machine learning and novel optimization techniques. Researchers are exploring new approaches to improve the efficiency and fairness of auctions, particularly in complex settings such as first-price auctions and sequential auctions. The development of online learning algorithms and dynamic mechanism design is enabling more effective allocation of resources and improved decision-making. Furthermore, studies on fair allocation are focusing on the design of algorithms that can ensure maximin share allocations and approximate pairwise maximin share fairness in various valuation models. Noteworthy papers in this area include:

• Optimizing Bidding Strategies in First-Price Auctions in Binary Feedback Setting with Predictions, which proposes a new algorithm that achieves zero regret under accurate predictions.
• Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Sequential Auctions under Unknown Environments, which develops an online reinforcement learning algorithm for the seller to learn the underlying MDP model and implement a mechanism that closely resembles the dynamic VCG mechanism.
• Polynomial-Time Approximation Schemes via Utility Alignment: Unit-Demand Pricing and More, which derives polynomial-time approximation schemes for several NP-hard stochastic optimization problems.