# Online Auction Design Using Distribution-Free Uncertainty Quantification with Applications to E-Commerce

arXiv: https://arxiv.org/abs/2405.07038 · HTML: https://mlsanity.com/paper/2405.07038/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 11 May 2024.

## Builds on (most cited first, 16 of 16)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Adaptive Conformal Inference Under Distribution Shift](https://mlsanity.com/paper/2106.00170/) (2021)
- [Conformal Prediction With Conditional Guarantees](https://mlsanity.com/paper/2305.12616/) (2023)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [The Sample Complexity of Revenue Maximization](https://mlsanity.com/paper/1502.00963/) (2015)
- [Settling the Sample Complexity of Single-parameter Revenue Maximization](https://mlsanity.com/paper/1904.04962/) (2019)
- [Multi-Item Mechanisms without Item-Independence: Learnability via Robustness](https://mlsanity.com/paper/1911.02146/) (2019)
- [The Sample Complexity of Auctions with Side Information](https://mlsanity.com/paper/1511.02296/) (2015)
- [Learning Multi-item Auctions with (or without) Samples](https://mlsanity.com/paper/1709.00228/) (2017)
- [Making the Most of Your Samples](https://mlsanity.com/paper/1407.2479/) (2014)
- [Learning Strategies in Decentralized Matching Markets under Uncertain Preferences](https://mlsanity.com/paper/2011.00159/) (2020)
- [Ironing in the Dark](https://mlsanity.com/paper/1511.06918/) (2015)
- [Learning in Multi-Stage Decentralized Matching Markets](https://mlsanity.com/paper/2102.06988/) (2021)
- [Incentive-Aware Recommender Systems in Two-Sided Markets](https://mlsanity.com/paper/2211.15381/) (2022)
- [Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis](https://mlsanity.com/paper/2103.07088/) (2021)

## Built on by (most cited first, 1 of 1)

- [Auto-bidding under Return-on-Spend Constraints with Uncertainty Quantification](https://mlsanity.com/paper/2509.16324/) (2025)

## Abstract (arXiv)

Online auction is a cornerstone of e-commerce, and a key challenge is designing incentive-compatible mechanisms that maximize expected revenue. Existing approaches often assume known bidder value distributions and fixed sets of bidders and items, but these assumptions rarely hold in real-world settings where bidder values are unknown, and the number of future participants is uncertain. In this paper, we introduce the Conformal Online Auction Design (COAD), a novel mechanism that maximizes revenue by quantifying uncertainty in bidder values without relying on known distributions. COAD incorporates both bidder and item features, using historical data to design an incentive-compatible mechanism for online auctions. Unlike traditional methods, COAD leverages distribution-free uncertainty quantification techniques and integrates machine learning methods, such as random forests, kernel methods, and deep neural networks, to predict bidder values while ensuring revenue guarantees. Moreover, COAD introduces bidder-specific reserve prices, based on the lower confidence bounds of bidder valuations, contrasting with the single reserve prices commonly used in the literature. We demonstrate the practical effectiveness of COAD through an application to real-world eBay auction data. Theoretical results and extensive simulation studies further validate the properties of our approach.