Learning Revenue-Maximizing Auctions with Neural Affine Maximizer

Learning truthful, revenue-maximizing auctions is a central challenge in automated mechanism design and differentiable economics. Existing learning approaches that guarantee truthfulness typically discretize the outcome space into a finite menu, which suffers from the “curse of dimensionality” in large-scale auctions. In this work, we propose *Neural Affine Maximizer* (NAM), a discretization-free mechanism for learning truthful auctions, as well as a novel algorithm to learn NAMs. NAM guarantees truthfulness by building on affine maximizer auctions (AMAs) while replacing the conventional finite menu with a boosting function over the outcome space. We then parameterize the boosting function with neural networks and derive unbiased gradient estimators to enable first-order optimization. Experiments on instances with up to $15$ buyers or $30$ goods show that NAM is either competitive with or outperforms existing approaches. Specifically, with $3$ buyers, $30$ goods, and additive valuations, NAM discovers a truthful auction achieving $10.6$% higher revenue and lower training time compared with state-of-the-art baselines.