# Advertising Media and Target Audience Optimization via High-dimensional Bandits

arXiv: https://arxiv.org/abs/2209.08403 · HTML: https://mlsanity.com/paper/2209.08403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 17 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits](https://mlsanity.com/paper/1402.0555/) (2014)
- [Mostly Exploration-Free Algorithms for Contextual Bandits](https://mlsanity.com/paper/1704.09011/) (2017)
- [Confidence Intervals and Hypothesis Testing for High-Dimensional Regression](https://mlsanity.com/paper/1306.3171/) (2013)
- [Randomized Exploration in Generalized Linear Bandits](https://mlsanity.com/paper/1906.08947/) (2019)
- [High-Dimensional Sparse Linear Bandits](https://mlsanity.com/paper/2011.04020/) (2020)
- [Doubly-Robust Lasso Bandit](https://mlsanity.com/paper/1907.11362/) (2019)
- [Sparsity-Agnostic Lasso Bandit](https://mlsanity.com/paper/2007.08477/) (2020)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [A Simple Unified Framework for High Dimensional Bandit Problems](https://mlsanity.com/paper/2102.09626/) (2021)
- [Comparison Lift: Bandit-based Experimentation System for Online Advertising](https://mlsanity.com/paper/2009.07899/) (2020)
- [Policy Gradient Optimization of Thompson Sampling Policies](https://mlsanity.com/paper/2006.16507/) (2020)
- [Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments](https://mlsanity.com/paper/1907.02178/) (2019)
- [A Revisit to De-biased Lasso for Generalized Linear Models](https://mlsanity.com/paper/2006.12778/) (2020)
- [Adaptive Experimentation with Delayed Binary Feedback](https://mlsanity.com/paper/2202.00846/) (2022)

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

- [TextBO: Bayesian Optimization in Language Space for Eval-Efficient Self-Improving AI](https://mlsanity.com/paper/2511.12063/) (2025)

## Abstract (arXiv)

We present a data-driven algorithm that advertisers can use to automate their digital ad-campaigns at online publishers. The algorithm enables the advertiser to search across available target audiences and ad-media to find the best possible combination for its campaign via online experimentation. The problem of finding the best audience-ad combination is complicated by a number of distinctive challenges, including (a) a need for active exploration to resolve prior uncertainty and to speed the search for profitable combinations, (b) many combinations to choose from, giving rise to high-dimensional search formulations, and (c) very low success probabilities, typically just a fraction of one percent. Our algorithm (designated LRDL, an acronym for Logistic Regression with Debiased Lasso) addresses these challenges by combining four elements: a multiarmed bandit framework for active exploration; a Lasso penalty function to handle high dimensionality; an inbuilt debiasing kernel that handles the regularization bias induced by the Lasso; and a semi-parametric regression model for outcomes that promotes cross-learning across arms. The algorithm is implemented as a Thompson Sampler, and to the best of our knowledge, it is the first that can practically address all of the challenges above. Simulations with real and synthetic data show the method is effective and document its superior performance against several benchmarks from the recent high-dimensional bandit literature.