mlsanityData to 7 Oct 2026

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

arXiv 1910.06403 ↗First posted 14 Oct 2019cs.LG cs.DC math.OCcited by 28

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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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Oct 2019.

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Citing papers per year: 2021: 9; 2022: 3; 2023: 2; 2024: 10; 2025: 0; 2026: 4.

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  1. 2022Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimizationcited by 96
  2. 2024Diffusion Models as Constrained Samplers for Optimization with Unknown Constraintscited by 33
  3. 2021Fast Adaptation with Linearized Neural Networkscited by 32
  4. 2021Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferencecited by 31
  5. 2021Geometry-aware Bayesian Optimization in Robotics using Riemannian Mat'ern Kernelscited by 24
  6. 2023qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson samplingcited by 11
  7. 2021Revisiting Bayesian Optimization in the light of the COCO benchmarkcited by 10
  8. 2024Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networkscited by 10
  9. 2021SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processescited by 9
  10. 2021BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameterscited by 6
  11. 2024Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modelingcited by 5
  12. 2021High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDBcited by 4
16 more
  1. 2021Cost-Efficient Online Hyperparameter Optimizationcited by 2
  2. 2021Bayesian Optimization for Min Max Optimizationcited by 2
  3. 2024Optimal Initialization of Batch Bayesian Optimizationcited by 2
  4. 2024Bayesian Optimization for Hyperparameters Tuning in Neural Networkscited by 2
  5. 2024Derivation of Closed Form of Expected Improvement for Gaussian Process Trained on Log-Transformed Objectivecited by 2
  6. 2024Dimensionality Reduction Techniques for Global Bayesian Optimisationcited by 1
  7. 2022Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Timecited by 0
  8. 2022Bayesian Optimization of 2D Echocardiography Segmentationcited by 0
  9. 2023Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holescited by 0
  10. 2024Multifidelity Cross-validationcited by 0
  11. 2024Estimating the Causal Effects of T Cell Receptorscited by 0
  12. 2024BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settingscited by 0
  13. 2026Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopycited by 0
  14. 2026BOxCrete: A Bayesian Optimization Open-Source AI Model for Concrete Strength Forecasting and Mix Optimizationcited by 0
  15. 2026Scalable Derivative Gaussian Processes via Exact Gradient Reductioncited by 0
  16. 2026La Agente 'Optima: Towards Agentic Self-Driving Laboratoriescited by 0