# HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments

arXiv: https://arxiv.org/abs/2510.15238 · HTML: https://mlsanity.com/paper/2510.15238/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.09756/) (2018)
- [Sustainable Online Reinforcement Learning for Auto-bidding](https://mlsanity.com/paper/2210.07006/) (2022)
- [Towards Efficient Auctions in an Auto-bidding World](https://mlsanity.com/paper/2103.13356/) (2021)
- [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [Bid Shading in The Brave New World of First-Price Auctions](https://mlsanity.com/paper/2009.01360/) (2020)
- [Lift-Based Bidding in Ad Selection](https://mlsanity.com/paper/1507.04811/) (2015)
- [Bid Shading by Win-Rate Estimation and Surplus Maximization](https://mlsanity.com/paper/2009.09259/) (2020)
- [Online Causal Inference for Advertising in Real-Time Bidding Auctions](https://mlsanity.com/paper/1908.08600/) (2019)
- [Why Do Competitive Markets Converge to First-Price Auctions?](https://mlsanity.com/paper/2001.06074/) (2020)
- [Multi-Platform Autobidding with and without Predictions](https://mlsanity.com/paper/2502.19317/) (2025)
- [Unbiased Lift-based Bidding System](https://mlsanity.com/paper/2007.04002/) (2020)
- [Multi-Platform Budget Management in Ad Markets with Non-IC Auctions](https://mlsanity.com/paper/2306.07352/) (2023)
- [MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising](https://mlsanity.com/paper/2403.02607/) (2024)
- [Beyond Advertising: Mechanism Design for Platform-Wide Marketing Service "QuanZhanTui"](https://mlsanity.com/paper/2507.02931/) (2025)
- [Uniform Pricing vs Pay as Bid in 100%-Renewables Electricity Markets: A Game-theoretical Analysis](https://mlsanity.com/paper/2305.12309/) (2023)
- [Platform Competition in the Autobidding World](https://mlsanity.com/paper/2405.02699/) (2024)
- [Double Distributionally Robust Bid Shading for First Price Auctions](https://mlsanity.com/paper/2410.14864/) (2024)

## Abstract (arXiv)

Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding. Auction mechanisms vary across channels in ranking rules (pure eCPM vs. UE-augmented scoring), pricing formats (first- vs. second-price), and bidding conventions (uniform vs. non-uniform), while advertisers impose shared campaign-level constraints. We propose HOB, which makes marginal cost (MC) computable and alignable across heterogeneous channels, especially for first-price auctions (FPA) with organic-paid coexistence, where existing bidding formulations do not yield a practical aligned MC form. At the global level, HOB derives channel-specific MC forms and coordinates disparate channels through a shared MC target. At the local level, HOB models free-win probability and winning-price uncertainty with a zero-inflated exponential distribution, yielding an efficient surplus-optimal bidding strategy for non-uniform first-price auctions. We show that any interior optimum satisfies MC equalization across channels. Experiments on a controlled offline benchmark, industrial log replay, and large-scale online A/B tests demonstrate that HOB consistently delivers significant performance gains. Deployed on a large-scale commercial DSP, HOB delivers a 3.0% lift in GMV while maintaining return on advertising spend (ROAS) constraints.