# BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

arXiv: https://arxiv.org/abs/1910.06403 · HTML: https://mlsanity.com/paper/1910.06403/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 30 of 30)

- [Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization](https://mlsanity.com/paper/2202.08450/) (2022)
- [Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints](https://mlsanity.com/paper/2402.18012/) (2024)
- [Fast Adaptation with Linearized Neural Networks](https://mlsanity.com/paper/2103.01439/) (2021)
- [Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference](https://mlsanity.com/paper/2102.11289/) (2021)
- [Geometry-aware Bayesian Optimization in Robotics using Riemannian Mat'ern Kernels](https://mlsanity.com/paper/2111.01460/) (2021)
- [qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling](https://mlsanity.com/paper/2310.15788/) (2023)
- [Revisiting Bayesian Optimization in the light of the COCO benchmark](https://mlsanity.com/paper/2103.16649/) (2021)
- [Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks](https://mlsanity.com/paper/2404.03099/) (2024)
- [SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes](https://mlsanity.com/paper/2106.06695/) (2021)
- [Deep Task-Based Analog-to-Digital Conversion](https://mlsanity.com/paper/2201.12634/) (2022)
- [BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters](https://mlsanity.com/paper/2112.08774/) (2021)
- [Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling](https://mlsanity.com/paper/2412.08776/) (2024)
- [High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB](https://mlsanity.com/paper/2103.16267/) (2021)
- [Cost-Efficient Online Hyperparameter Optimization](https://mlsanity.com/paper/2101.06590/) (2021)
- [Bayesian Optimization for Min Max Optimization](https://mlsanity.com/paper/2107.13772/) (2021)
- [Optimal Initialization of Batch Bayesian Optimization](https://mlsanity.com/paper/2404.17997/) (2024)
- [Bayesian Optimization for Hyperparameters Tuning in Neural Networks](https://mlsanity.com/paper/2410.21886/) (2024)
- [Derivation of Closed Form of Expected Improvement for Gaussian Process Trained on Log-Transformed Objective](https://mlsanity.com/paper/2411.18095/) (2024)
- [Estimating the Causal Effects of T Cell Receptors](https://mlsanity.com/paper/2410.14127/) (2024)
- [Dimensionality Reduction Techniques for Global Bayesian Optimisation](https://mlsanity.com/paper/2412.09183/) (2024)
- [La Agente 'Optima: Towards Agentic Self-Driving Laboratories](https://mlsanity.com/paper/2609.04564/) (2026)
- [Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Time](https://mlsanity.com/paper/2211.05601/) (2022)
- [Bayesian Optimization of 2D Echocardiography Segmentation](https://mlsanity.com/paper/2211.09888/) (2022)
- [Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes](https://mlsanity.com/paper/2303.14291/) (2023)
- [Multifidelity Cross-validation](https://mlsanity.com/paper/2407.01495/) (2024)
- [BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings](https://mlsanity.com/paper/2412.00308/) (2024)
- [Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy](https://mlsanity.com/paper/2603.16715/) (2026)
- [BOxCrete: A Bayesian Optimization Open-Source AI Model for Concrete Strength Forecasting and Mix Optimization](https://mlsanity.com/paper/2603.21525/) (2026)
- [Scalable Derivative Gaussian Processes via Exact Gradient Reduction](https://mlsanity.com/paper/2606.02909/) (2026)
- [Uncertainty quantification in mechanics: A unified Bayesian perspective](https://mlsanity.com/paper/2607.18734/) (2026)

## Abstract (arXiv)

Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, auto-differentiation, and variance reduction techniques. BoTorch's modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, simplifying implementation of new acquisition functions. Our approach is backed by novel theoretical convergence results and made practical by a distinctive algorithmic foundation that leverages fast predictive distributions, hardware acceleration, and deterministic optimization. We also propose a novel "one-shot" formulation of the Knowledge Gradient, enabled by a combination of our theoretical and software contributions. In experiments, we demonstrate the improved sample efficiency of BoTorch relative to other popular libraries.