# Deep Learning-powered Iterative Combinatorial Auctions

arXiv: https://arxiv.org/abs/1907.05771 · HTML: https://mlsanity.com/paper/1907.05771/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022)
- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [NOMU: Neural Optimization-based Model Uncertainty](https://mlsanity.com/paper/2102.13640/) (2021)
- [Learning Revenue-Maximizing Auctions With Differentiable Matching](https://mlsanity.com/paper/2106.07877/) (2021)
- [Learning to Mitigate AI Collusion on Economic Platforms](https://mlsanity.com/paper/2202.07106/) (2022)
- [Bayesian Optimization-based Combinatorial Assignment](https://mlsanity.com/paper/2208.14698/) (2022)
- [Accelerated Preference Elicitation with LLM-Based Proxies](https://mlsanity.com/paper/2501.14625/) (2025)
- [LLM-Powered Preference Elicitation in Combinatorial Assignment](https://mlsanity.com/paper/2502.10308/) (2025)
- [Machine Learning-Powered Combinatorial Clock Auction](https://mlsanity.com/paper/2308.10226/) (2023)
- [Machine Learning-Powered Course Allocation](https://mlsanity.com/paper/2210.00954/) (2022)
- [Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All](https://mlsanity.com/paper/2411.09355/) (2024)
- [Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications](https://mlsanity.com/paper/2401.05683/) (2024)

## Abstract (arXiv)

In this paper, we study the design of deep learning-powered iterative combinatorial auctions (ICAs). We build on prior work where preference elicitation was done via kernelized support vector regressions (SVRs). However, the SVR-based approach has limitations because it requires solving a machine learning (ML)-based winner determination problem (WDP). With expressive kernels (like gaussians), the ML-based WDP cannot be solved for large domains. While linear or quadratic kernels have better computational scalability, these kernels have limited expressiveness. In this work, we address these shortcomings by using deep neural networks (DNNs) instead of SVRs. We first show how the DNN-based WDP can be reformulated into a mixed integer program (MIP). Second, we experimentally compare the prediction performance of DNNs against SVRs. Third, we present experimental evaluations in two medium-sized domains which show that even ICAs based on relatively small-sized DNNs lead to higher economic efficiency than ICAs based on kernelized SVRs. Finally, we show that our DNN-powered ICA also scales well to very large CA domains.