# Revenue Optimization with Approximate Bid Predictions

arXiv: https://arxiv.org/abs/1706.04732 · HTML: https://mlsanity.com/paper/1706.04732/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2017.

## Built on by (most cited first, 21 of 21)

- [Improving Online Algorithms via ML Predictions](https://mlsanity.com/paper/2407.17712/) (2024)
- [Online Algorithms with Multiple Predictions](https://mlsanity.com/paper/2205.03921/) (2022)
- [Customizing ML Predictions for Online Algorithms](https://mlsanity.com/paper/2205.08715/) (2022)
- [Algorithms with Prediction Portfolios](https://mlsanity.com/paper/2210.12438/) (2022)
- [Can Q-Learning be Improved with Advice?](https://mlsanity.com/paper/2110.13052/) (2021)
- [A Regression Approach to Learning-Augmented Online Algorithms](https://mlsanity.com/paper/2205.08717/) (2022)
- [Double Coverage with Machine-Learned Advice](https://mlsanity.com/paper/2103.01640/) (2021)
- [Robustification of Online Graph Exploration Methods](https://mlsanity.com/paper/2112.05422/) (2021)
- [Adversarial Deep Learning for Online Resource Allocation](https://mlsanity.com/paper/2111.10285/) (2021)
- [Online Ad Allocation with Predictions](https://mlsanity.com/paper/2302.01827/) (2023)
- [Non-clairvoyant Scheduling with Partial Predictions](https://mlsanity.com/paper/2405.01013/) (2024)
- [Loss Functions for Discrete Contextual Pricing with Observational Data](https://mlsanity.com/paper/2111.09933/) (2021)
- [Improved Bounds for Online Facility Location with Predictions](https://mlsanity.com/paper/2107.08277/) (2021)
- [Contextual Pandora's Box](https://mlsanity.com/paper/2205.13114/) (2022)
- [Regulatory Instruments for Fair Personalized Pricing](https://mlsanity.com/paper/2202.04245/) (2022)
- [Sequential Ski Rental Problem](https://mlsanity.com/paper/2104.06050/) (2021)
- [Best of Many in Both Worlds: Online Resource Allocation with Predictions under Unknown Arrival Model](https://mlsanity.com/paper/2402.13530/) (2024)
- [Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data](https://mlsanity.com/paper/2202.10944/) (2022)
- [Online TSP with Predictions](https://mlsanity.com/paper/2206.15364/) (2022)
- [Constrained Online Two-stage Stochastic Optimization: Algorithm with (and without) Predictions](https://mlsanity.com/paper/2401.01077/) (2024)
- [Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications](https://mlsanity.com/paper/2609.04787/) (2026)

## Abstract (arXiv)

In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. We further bound the gap between the expected bid and revenue in terms of the average loss of the predictor. This is the first result that formally relates the revenue gained to the quality of a standard machine learned model.