# iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding

arXiv: https://arxiv.org/abs/2009.13605 · HTML: https://mlsanity.com/paper/2009.13605/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All](https://mlsanity.com/paper/2411.09355/) (2024)

## Abstract (arXiv)

Preference elicitation is a major challenge in large combinatorial auctions because the bundle space grows exponentially in the number of items. Recent work has used machine learning (ML) algorithms to identify a small set of bundles to query from each bidder. However, a shortcoming of this prior work is that bidders must submit exact values for the queried bundles, which can be quite costly. To address this, we propose iMLCA, a new ML-powered iterative combinatorial auction with interval bidding (i.e., where bidders submit upper and lower bounds instead of exact values). To steer the auction towards an efficient allocation, we introduce a price-based activity rule, asking bidders to tighten bounds on relevant bundles only. In our experiments, iMLCA achieves the same allocative efficiency as the prior ML-based auction that uses exact bidding. Moreover, it outperforms the well-known combinatorial clock auction in a realistically-sized domain.