# BAT: Benchmark for Auto-bidding Task

arXiv: https://arxiv.org/abs/2505.08485 · HTML: https://mlsanity.com/paper/2505.08485/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 13 May 2025.

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

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions](https://mlsanity.com/paper/2007.04568/) (2020)
- [Real-time Bidding for Online Advertising: Measurement and Analysis](https://mlsanity.com/paper/1306.6542/) (2013)
- [Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics](https://mlsanity.com/paper/2301.13306/) (2023)
- [Bid Prediction in Repeated Auctions with Learning](https://mlsanity.com/paper/2007.13193/) (2020)
- [Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising](https://mlsanity.com/paper/1305.3011/) (2013)
- [Learning optimization models in the presence of unknown relations](https://mlsanity.com/paper/1401.1061/) (2014)
- [Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising](https://mlsanity.com/paper/1808.03737/) (2018)
- [Bid Shading by Win-Rate Estimation and Surplus Maximization](https://mlsanity.com/paper/2009.09259/) (2020)
- [Why Do Competitive Markets Converge to First-Price Auctions?](https://mlsanity.com/paper/2001.06074/) (2020)
- [Sequential Selection of Correlated Ads by POMDPs](https://mlsanity.com/paper/1307.3284/) (2013)
- [Incentive Mechanism Design for ROI-constrained Auto-bidding](https://mlsanity.com/paper/2012.02652/) (2020)
- [Users and Contemporary SERPs: A (Re-)Investigation Examining User Interactions and Experiences](https://mlsanity.com/paper/2207.13117/) (2022)
- [Optimization of a SSP's Header Bidding Strategy using Thompson Sampling](https://mlsanity.com/paper/1807.03299/) (2018)

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

- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [Uncertainty Quantification of Click and Conversion Estimates for the Autobidding](https://mlsanity.com/paper/2603.01825/) (2026)

## Abstract (arXiv)

The optimization of bidding strategies for online advertising slot auctions presents a critical challenge across numerous digital marketplaces. A significant obstacle to the development, evaluation, and refinement of real-time autobidding algorithms is the scarcity of comprehensive datasets and standardized benchmarks. To address this deficiency, we present an auction benchmark encompassing the two most prevalent auction formats. We implement a series of robust baselines on a novel dataset, addressing the most salient Real-Time Bidding (RTB) problem domains: budget pacing uniformity and Cost Per Click (CPC) constraint optimization. This benchmark provides a user-friendly and intuitive framework for researchers and practitioners to develop and refine innovative autobidding algorithms, thereby facilitating advancements in the field of programmatic advertising. The implementation and additional resources can be accessed at the following repository (https://github.com/avito-tech/bat-autobidding-benchmark, https://doi.org/10.5281/zenodo.14794182).