A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Not on the map. Why?
Abstract (arXiv)
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables simultaneously, model misspecification, and lastly fast massive parallelisation. To address these challenges, we introduce a versatile and modular framework for batch Bayesian optimisation via probabilistic lifting with kernel quadrature, called SOBER, which we present as a Python library based on GPyTorch/BoTorch. Our framework offers the following unique benefits: (1) Versatility in downstream tasks under a unified approach. (2) A gradient-free sampler, which does not require the gradient of acquisition functions, offering domain-agnostic sampling (e.g., discrete and mixed variables, non-Euclidean space). (3) Flexibility in domain prior distribution. (4) Adaptive batch size (autonomous determination of the optimal batch size). (5) Robustness against a misspecified reproducing kernel Hilbert space. (6) Natural stopping criterion.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 71 of its 130 references that are in the map. First posted on arXiv 18 Apr 2024.
Builds on
71 of 130 references.
References · 71
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
- 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 765
- 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
- 2016The CMA Evolution Strategy: A Tutorialcited by 377
- 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 374
- 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 364
- 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 206
- 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functionscited by 204
- 2015High Dimensional Bayesian Optimisation and Bandits via Additive Modelscited by 139
- 2015On the Equivalence between Kernel Quadrature Rules and Random Feature Expansionscited by 138
- 2020Efficiently Sampling Functions from Gaussian Process Posteriorscited by 127
38 more
- 2018Maximizing acquisition functions for Bayesian optimizationcited by 114
- 2014Bayesian Optimization with Unknown Constraintscited by 109
- 2015Batch Bayesian Optimization via Local Penalizationcited by 104
- 2017Constrained Bayesian Optimization with Noisy Experimentscited by 104
- 2020Scalable Constrained Bayesian Optimizationcited by 84
- 2019Combinatorial Bayesian Optimization using the Graph Cartesian Productcited by 79
- 2017Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Spacecited by 76
- 2022πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimizationcited by 70
- 2015Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Modelscited by 61
- 2015Probabilistic Numerics and Uncertainty in Computationscited by 60
- 2014Control functionals for Monte Carlo integrationcited by 59
- 2019Bayesian Optimisation over Multiple Continuous and Categorical Inputscited by 56
- 2021Misspecified Gaussian Process Bandit Optimizationcited by 55
- 2021Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spacescited by 53
- 2015Predictive Entropy Search for Bayesian Optimization with Unknown Constraintscited by 51
- 2019No-Regret Bayesian Optimization with Unknown Hyperparameterscited by 51
- 2015A General Framework for Constrained Bayesian Optimization using Information-based Searchcited by 50
- 2022Constrained Efficient Global Optimization of Expensive Black-box Functionscited by 49
- 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
- 2021GIBBON: General-purpose Information-Based Bayesian OptimisatioNcited by 47
- 2021Kernel Thinningcited by 47
- 2021Bayesian decision-making under misspecified priors with applications to meta-learningcited by 44
- 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 41
- 2022Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterizationcited by 37
- 2016Batched Gaussian Process Bandit Optimization via Determinantal Point Processescited by 33
- 2023A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methodscited by 32
- 2018Regret bounds for meta Bayesian optimization with an unknown Gaussian process priorcited by 31
- 2021Generalized Kernel Thinningcited by 29
- 2023Bayesian Synthetic Likelihoodcited by 29
- 2019Kernel quadrature with DPPscited by 27
- 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient Descentcited by 27
- 2023Looping in the Human Collaborative and Explainable Bayesian Optimizationcited by 27
- 2015Probabilistic Integration: A Role in Statistical Computation?cited by 26
- 2021Positively Weighted Kernel Quadrature via Subsamplingcited by 25
- 2020Gaussian Processes on Graphs via Spectral Kernel Learningcited by 24
- 2021Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processescited by 24
- 2020Finding Global Minima via Kernel Approximationscited by 23
- 2019Convergence Guarantees for Adaptive Bayesian Quadrature Methodscited by 22
50 most cited of 71 All 71 (JSON)
Built on by
Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.