# Towards Efficient Auctions in an Auto-bidding World

arXiv: https://arxiv.org/abs/2103.13356 · HTML: https://mlsanity.com/paper/2103.13356/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2021.

## Built on by (most cited first, 19 of 19)

- [Auctions Between Regret-Minimizing Agents](https://mlsanity.com/paper/2110.11855/) (2021)
- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents](https://mlsanity.com/paper/2112.07640/) (2021)
- [Online Bidding Algorithms for Return-on-Spend Constrained Advertisers](https://mlsanity.com/paper/2208.13713/) (2022)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [GAS: Generative Auto-bidding with Post-training Search](https://mlsanity.com/paper/2412.17018/) (2024)
- [Games with Payments between Learning Agents](https://mlsanity.com/paper/2405.20880/) (2024)
- [Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization](https://mlsanity.com/paper/2508.02002/) (2025)
- [AIGB: Generative Auto-bidding via Conditional Diffusion Modeling](https://mlsanity.com/paper/2405.16141/) (2024)
- [Online Bidding under RoS Constraints without Knowing the Value](https://mlsanity.com/paper/2503.03195/) (2025)
- [Online Ad Procurement in Non-stationary Autobidding Worlds](https://mlsanity.com/paper/2307.05698/) (2023)
- [Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search](https://mlsanity.com/paper/2509.15927/) (2025)
- [PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding](https://mlsanity.com/paper/2602.08261/) (2026)
- [Online Bidding Algorithms with Strict Return on Spend (ROS) Constraint](https://mlsanity.com/paper/2502.05599/) (2025)
- [HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments](https://mlsanity.com/paper/2510.15238/) (2025)
- [DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation](https://mlsanity.com/paper/2606.14192/) (2026)
- [AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization](https://mlsanity.com/paper/2607.17281/) (2026)
- [Beyond Single-Episode Optimization: Sliding-Window Aware Generative Auto-Bidding for Long-Term Advertising Effectiveness](https://mlsanity.com/paper/2607.25233/) (2026)
- [Inference Auctions](https://mlsanity.com/paper/2609.40070/) (2026)

## Abstract (arXiv)

Auto-bidding has become one of the main options for bidding in online advertisements, in which advertisers only need to specify high-level objectives and leave the complex task of bidding to auto-bidders. In this paper, we propose a family of auctions with boosts to improve welfare in auto-bidding environments with both return on ad spend constraints and budget constraints. Our empirical results validate our theoretical findings and show that both the welfare and revenue can be improved by selecting the weight of the boosts properly.