# Black-Box Optimization with Implicit Constraints for Public Policy

arXiv: https://arxiv.org/abs/2310.18449 · HTML: https://mlsanity.com/paper/2310.18449/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 51 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1403.5607/) (2014)
- [Constrained Bayesian Optimization with Noisy Experiments](https://mlsanity.com/paper/1706.07094/) (2017)
- [Scalable Constrained Bayesian Optimization](https://mlsanity.com/paper/2002.08526/) (2020)
- [Local Latent Space Bayesian Optimization over Structured Inputs](https://mlsanity.com/paper/2201.11872/) (2022)
- [A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization](https://mlsanity.com/paper/2111.05040/) (2021)
- [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](https://mlsanity.com/paper/1903.06694/) (2019)
- [Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables](https://mlsanity.com/paper/1910.01688/) (2019)
- [Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces](https://mlsanity.com/paper/2102.07188/) (2021)
- [Improving black-box optimization in VAE latent space using decoder uncertainty](https://mlsanity.com/paper/2107.00096/) (2021)
- [Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems](https://mlsanity.com/paper/2012.03448/) (2020)
- [Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces](https://mlsanity.com/paper/2111.01186/) (2021)
- [Global Optimization with Parametric Function Approximation](https://mlsanity.com/paper/2211.09100/) (2022)
- [Data-Driven Decision Making in COVID-19 Response: A Survey](https://mlsanity.com/paper/2202.11435/) (2022)
- [Data-Driven Optimization for Atlanta Police Zone Design](https://mlsanity.com/paper/2104.00535/) (2021)
- [Crime Event Embedding with Unsupervised Feature Selection](https://mlsanity.com/paper/1806.06095/) (2018)
- [A Graph-Prediction-Based Approach for Debiasing Underreported Data](https://mlsanity.com/paper/2307.07898/) (2023)
- [Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization](https://mlsanity.com/paper/2005.11257/) (2020)

## Built on by (most cited first, 4 of 4)

- [Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization](https://mlsanity.com/paper/2507.00480/) (2025)
- [Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation](https://mlsanity.com/paper/2409.11535/) (2024)
- [Can LLMs Identify Tax Abuse?](https://mlsanity.com/paper/2508.20097/) (2025)
- [Deciding When to Decide: Testing Operational Suboptimality Under Distributional Shift](https://mlsanity.com/paper/2608.29465/) (2026)

## Abstract (arXiv)

Black-box optimization (BBO) has become increasingly relevant for tackling complex decision-making problems, especially in public policy domains such as police redistricting. However, its broader application in public policymaking is hindered by the complexity of defining feasible regions and the high-dimensionality of decisions. This paper introduces a novel BBO framework, termed as the Conditional And Generative Black-box Optimization (CageBO). This approach leverages a conditional variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a simplified, constraint-free latent space. The CageBO efficiently handles the implicit constraints often found in public policy applications, allowing for optimization in the latent space while evaluating objectives in the original space. We validate our method through a case study on large-scale police redistricting problems in Atlanta, Georgia. Our results reveal that our CageBO offers notable improvements in performance and efficiency compared to the baselines.