Bayesian Optimisation

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Most implemented papers archive 2025-07-28

30 shown of 97 papers with code (221 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Max-value Entropy Search for Efficient Bayesian Optimization
  6 Mar 2017 4 repositories listed
  We propose a new criterion, Max-value Entropy Search (MES), that instead uses the information about the maximum function value.
• Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation
  25 Jul 2022 3 repositories listed
  In this paper we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation.
• HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
  7 Dec 2020 3 repositories listed
  Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers.
• Sample-efficient Bayesian Optimisation Using Known Invariances
  22 Oct 2024 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 1 pointer-only (licence)
  We derive a bound on the maximum information gain of these invariant kernels, and provide novel upper and lower bounds on the number of observations required for invariance-aware BO algorithms to achieve ϵ-optimality.
• Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations
  11 Jan 2024 2 repositories listed
  Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics.
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
  25 May 2023 2 repositories listed
  We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
• Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
  30 Jan 2023 2 repositories listed
  Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems.
• GAUCHE: A Library for Gaussian Processes in Chemistry
  6 Dec 2022 2 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
• High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
  7 Jun 2021 2 repositories listed
  We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
• Neural Architecture Generator Optimization
  3 Apr 2020 2 repositories listed
  Neural Architecture Search (NAS) was first proposed to achieve state-of-the-art performance through the discovery of new architecture patterns, without human intervention.
• BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
  14 Oct 2019 2 repositories listed
  Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
• On the Expressiveness of Approximate Inference in Bayesian Neural Networks
  2 Sep 2019 2 repositories listed Syntology ran 1 of 13 samples · 12 unverified
  While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly understood.
• Bayesian Optimisation over Multiple Continuous and Categorical Inputs
  20 Jun 2019 2 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges.
• Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
  23 Jun 2015 2 repositories listed
  We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods.
• Nested Expectations with Kernel Quadrature
  25 Feb 2025 1 repository listed
  This paper considers the challenging computational task of estimating nested expectations.
• Mean-Field Bayesian Optimisation
  17 Feb 2025 1 repository listed
  We address the problem of optimising the average payoff for a large number of cooperating agents, where the payoff function is unknown and treated as a black box.
• Nonmyopic Global Optimisation via Approximate Dynamic Programming
  6 Dec 2024 1 repository listed
  Bayesian optimisation, one of the most well-known techniques, typically employs Gaussian processes as surrogate models, leveraging their probabilistic nature to balance exploration and exploitation.
• Residual Deep Gaussian Processes on Manifolds
  31 Oct 2024 1 repository listed
  We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks.
• Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes
  18 Oct 2024 1 repository listed
  Accurate uncertainty quantification for causal effects is essential for robust decision making in complex systems, but remains challenging in non-parametric settings.
• Principled Bayesian Optimisation in Collaboration with Human Experts
  14 Oct 2024 1 repository listed Syntology ran 3 of 9 samples · 6 unverified · 9 pointer-only (licence)
  Experts' labels are often costly, requiring efficient use of their efforts, and can at the same time be unreliable, requiring careful adjustment of the degree to which any expert is trusted.
• Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
  14 Oct 2024 1 repository listed
  We formally derive a cumulative regret bound of LB and compare it with the regret of an oracle BO algorithm using the optimal length scale.
• A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
  18 Apr 2024 1 repository listed Syntology ran 7 of 11 samples · 4 unverified
  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…
• Personalized LLM Response Generation with Parameterized Memory Injection
  4 Apr 2024 1 repository listed
  Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language.
• On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions
  5 Feb 2024 1 repository listed
  ENVBO finds solutions for the full domain of the environmental variable that outperforms results from optimisation algorithms that only focus on a fixed environmental value in all but one case while using a fraction of…
• Automated Machine Learning for Positive-Unlabelled Learning
  12 Jan 2024 1 repository listed
  Positive-Unlabelled (PU) learning is a growing field of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances, which can be in reality positive or negative,…
• Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems
  5 Dec 2023 1 repository listed Syntology ran 6 of 10 samples · 4 unverified
  Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation.
• Data-driven Prior Learning for Bayesian Optimisation
  24 Nov 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  We replace this assumption with a weaker one only requiring the shape of the optimisation landscape to be similar, and analyse the recent method Prior Learning for Bayesian Optimisation - PLeBO - in this setting.
• Robust and Conjugate Gaussian Process Regression
  1 Nov 2023 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise.
• Stochastic Gradient Descent for Gaussian Processes Done Right
  31 Oct 2023 1 repository listed Syntology ran 15 of 19 samples · 4 unverified
  We study the use of stochastic gradient descent for solving this linear system, and show that when \emph{done right} -- by which we mean using specific insights from the optimisation and kernel communities -- stochastic…
• Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach
  9 Jun 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation.

Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.