# Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All

arXiv: https://arxiv.org/abs/2411.09355 · HTML: https://mlsanity.com/paper/2411.09355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 59 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Builds on (most cited first, 22 of 22)

- [A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case](https://mlsanity.com/paper/1910.01635/) (2019)
- [How do infinite width bounded norm networks look in function space?](https://mlsanity.com/paper/1902.05040/) (2019)
- [What Kinds of Functions do Deep Neural Networks Learn? Insights from Variational Spline Theory](https://mlsanity.com/paper/2105.03361/) (2021)
- [The Sample Complexity of Revenue Maximization](https://mlsanity.com/paper/1502.00963/) (2015)
- [Gradient Dynamics of Shallow Univariate ReLU Networks](https://mlsanity.com/paper/1906.07842/) (2019)
- [Truthful Aggregation of LLMs with an Application to Online Advertising](https://mlsanity.com/paper/2405.05905/) (2024)
- [Deep Learning-powered Iterative Combinatorial Auctions](https://mlsanity.com/paper/1907.05771/) (2019)
- [Generalization Guarantees for Multi-item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms](https://mlsanity.com/paper/1705.00243/) (2017)
- [Machine Learning-powered Iterative Combinatorial Auctions](https://mlsanity.com/paper/1911.08042/) (2019)
- [Bayesian Optimization-based Combinatorial Assignment](https://mlsanity.com/paper/2208.14698/) (2022)
- [Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment](https://mlsanity.com/paper/2109.15117/) (2021)
- [Fourier Analysis-based Iterative Combinatorial Auctions](https://mlsanity.com/paper/2009.10749/) (2020)
- [The Pseudo-Dimension of Near-Optimal Auctions](https://mlsanity.com/paper/1506.03684/) (2015)
- [Fast Iterative Combinatorial Auctions via Bayesian Learning](https://mlsanity.com/paper/1809.05340/) (2018)
- [How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization](https://mlsanity.com/paper/2112.15577/) (2021)
- [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)
- [How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part II: the Multi-D Case of Two Layers with Random First Layer](https://mlsanity.com/paper/2303.11454/) (2023)
- [A Bayesian Clearing Mechanism for Combinatorial Auctions](https://mlsanity.com/paper/1712.05291/) (2017)
- [iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding](https://mlsanity.com/paper/2009.13605/) (2020)
- [Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.19420/) (2024)
- [Efficient Preference Elicitation in Iterative Combinatorial Auctions with Many Participants](https://mlsanity.com/paper/2403.19075/) (2024)

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

- [LLM-Powered Preference Elicitation in Combinatorial Assignment](https://mlsanity.com/paper/2502.10308/) (2025)
- [The Pseudo-Dimension of Contracts](https://mlsanity.com/paper/2501.14474/) (2025)

## Abstract (arXiv)

We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, recent work has proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most critical information from bidders to maximize efficiency. However, while the SOTA ML-based algorithms elicit bidders' preferences via value queries, ICAs that are used in practice elicit information via demand queries. In this paper, we introduce a novel ML algorithm that provably makes use of the full information from both value and demand queries, and we show via experiments that combining both query types results in significantly better learning performance in practice. Building on these insights, we present MLHCA, a new ML-powered auction that uses value and demand queries. MLHCA significantly outperforms the previous SOTA, reducing efficiency loss by up to a factor 10, with up to 58% fewer queries. Thus, MLHCA achieves large efficiency improvements while also reducing bidders' cognitive load, establishing a new benchmark for both practicability and efficiency. Our code is available at https://github.com/marketdesignresearch/MLHCA.