# Fast Iterative Combinatorial Auctions via Bayesian Learning

arXiv: https://arxiv.org/abs/1809.05340 · HTML: https://mlsanity.com/paper/1809.05340/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [Accelerated Preference Elicitation with LLM-Based Proxies](https://mlsanity.com/paper/2501.14625/) (2025)
- [Machine Learning-Powered Combinatorial Clock Auction](https://mlsanity.com/paper/2308.10226/) (2023)
- [Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All](https://mlsanity.com/paper/2411.09355/) (2024)

## Abstract (arXiv)

Iterative combinatorial auctions (CAs) are often used in multi-billion dollar domains like spectrum auctions, and speed of convergence is one of the crucial factors behind the choice of a specific design for practical applications. To achieve fast convergence, current CAs require careful tuning of the price update rule to balance convergence speed and allocative efficiency. Brero and Lahaie (2018) recently introduced a Bayesian iterative auction design for settings with single-minded bidders. The Bayesian approach allowed them to incorporate prior knowledge into the price update algorithm, reducing the number of rounds to convergence with minimal parameter tuning. In this paper, we generalize their work to settings with no restrictions on bidder valuations. We introduce a new Bayesian CA design for this general setting which uses Monte Carlo Expectation Maximization to update prices at each round of the auction. We evaluate our approach via simulations on CATS instances. Our results show that our Bayesian CA outperforms even a highly optimized benchmark in terms of clearing percentage and convergence speed.