Bayesian optimization identifies cement-salt hydrate composites achieving up to five times higher specific energy than prior cement-based TCES materials, with LiCl-based formulations reaching 458 kJ/kg.
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A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
30 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences.
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cs.LG 9 cs.AI 3 stat.ML 3 math.OC 2 cond-mat.mtrl-sci 1 cs.CL 1 cs.CV 1 cs.ET 1 cs.HC 1 cs.RO 1roles
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background 4representative citing papers
A human-in-the-loop Pareto optimization framework characterizes trade-offs between performance and challenge in assist-as-needed motor training, enabling protocol design and fair evaluation even when users cannot complete tasks unaided.
EARL-BO uses RL with an Attention-DeepSets encoder and end-to-end on-policy multi-task fine-tuning to approximate near-optimal multi-step lookahead policies for high-dimensional black-box optimization.
First autonomous 6D phase-space tomography system at LCLS-II achieves real-time beam reconstructions every 5-10 minutes via ML control and generative analysis.
PALM-Mean combines sign-aware piecewise-linear relaxations of locally important kernel terms with closed-form analytic bounds on the rest inside a reduced-space branch-and-bound framework, yielding valid lower bounds and ε-global convergence for GP posterior mean optimization.
Develops RBS and MSLS heuristics exploiting follower optimality properties for bilevel uniform parallel machine scheduling with up to 500 jobs.
LGBO integrates LLM semantic preferences continuously into Bayesian optimization iterations, with a theoretical worst-case guarantee and empirical gains including 90% of best value in 6 iterations on a wet-lab battery task.
Active learning strategies for preference-based MPC objective learning achieve better closed-loop alignment with human preferences using fewer queries than random sampling in numerical tests.
A morphing-based design space explored via Bayesian optimization yields swimming profiles with 49-57% peak propulsive efficiency, 16-35% above standard anguilliform and carangiform references.
The paper introduces an anchor-based heteroscedastic noise model for PBO that maps user uncertainty via KDE on reliable examples, incorporates it into GP surrogates, and derives risk-averse acquisition functions including a risk-adjusted EUBO variant that preserves one-step Bayes-optimality up to an
ADKO is a decentralized framework where agents share compact GP-derived tokens and LM insights to achieve collaborative Bayesian optimization with a decomposed regret bound that includes compression and approximation losses.
Preference uncertainty is modeled as random variables that induce a distribution over Pareto-optimal designs, analyzed via Sobol' indices, Shapley values, and Fréchet variance to assess decision stability in ground vehicle design.
VPL learns individualized vibrotactile preferences efficiently via uncertainty-aware Gaussian process models and active query selection in a 13-participant user study on an Xbox controller.
Integrating Stein variational gradient descent into EDAs introduces repulsion among particles to jointly explore multiple optima in discrete black-box optimization, with competitive or superior results on large-scale problems.
A neural model learns iterative refinement from noisy samples and spline inputs to find global minima, reporting 8.05% mean error on multi-modal tests versus 36.24% for spline initialization alone.
A new ensemble Gaussian process method enforces physical constraints via weighted penalties and quadrature averaging to model noisy quantum many-body data more accurately than standard GPs.
Trust calibration in agentic tool use is cast as preferential Bayesian optimization over a latent human risk-tolerance function observed through binary approve/deny feedback with a probit likelihood.
Bayesian optimization enables adaptive network pruning rates in lifelong learning, performing heavier pruning on small/simple tasks and milder on large/complex ones.
A two-stage GP approach with virtual samples and posterior adjustment factors incorporates per-variable monotonic hunches into Bayesian optimization while preserving convergence guarantees, showing faster convergence on simulations and real polymer/scaffolding tasks.
Adaptive GLM with MQLE and GP regression with UCB for dynamic insurance pricing, showing parameter convergence and regret analysis under delayed claims.
A multi-objective Bayesian optimization framework co-optimizes CIM crossbar hardware and DNN parameters for VGG8/CIFAR-10 and VGG16/Tiny-ImageNet, achieving comparable accuracy with up to 65% smaller area and 52% lower energy.
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
Optimization study for muon tomography cargo scanner using TomOpt Bayesian optimization and GEANT4 simulations to enhance border security detection.
Introduces directionally constrained Bayesian optimization combining local and global search to improve optimum finding within evaluation limits.
citing papers explorer
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High-Throughput Bayesian Optimization of Cement-Salt Hydrates Composites for Seasonal Thermochemical Energy Storage
Bayesian optimization identifies cement-salt hydrate composites achieving up to five times higher specific energy than prior cement-based TCES materials, with LiCl-based formulations reaching 458 kJ/kg.
-
Human-in-the-Loop Pareto Optimization: Trade-off Characterization for Assist-as-Needed Training and Performance Evaluation
A human-in-the-loop Pareto optimization framework characterizes trade-offs between performance and challenge in assist-as-needed motor training, enabling protocol design and fair evaluation even when users cannot complete tasks unaided.
-
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
EARL-BO uses RL with an Attention-DeepSets encoder and end-to-end on-policy multi-task fine-tuning to approximate near-optimal multi-step lookahead policies for high-dimensional black-box optimization.
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Autonomous operation of the DIAG0 diagnostic line for 6D phase-space monitoring at LCLS-II
First autonomous 6D phase-space tomography system at LCLS-II achieves real-time beam reconstructions every 5-10 minutes via ML control and generative analysis.
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An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
PALM-Mean combines sign-aware piecewise-linear relaxations of locally important kernel terms with closed-form analytic bounds on the rest inside a reduced-space branch-and-bound framework, yielding valid lower bounds and ε-global convergence for GP posterior mean optimization.
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Heuristic approaches for solving a bilevel optimistic scheduling problem on parallel machines
Develops RBS and MSLS heuristics exploiting follower optimality properties for bilevel uniform parallel machine scheduling with up to 500 jobs.
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Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
LGBO integrates LLM semantic preferences continuously into Bayesian optimization iterations, with a theoretical worst-case guarantee and empirical gains including 90% of best value in 6 iterations on a wet-lab battery task.
-
Active Learning MPC Objective Functions from Preferences
Active learning strategies for preference-based MPC objective learning achieve better closed-loop alignment with human preferences using fewer queries than random sampling in numerical tests.
-
Optimized Fish Locomotion using Design-by-Morphing and Bayesian Optimization
A morphing-based design space explored via Bayesian optimization yields swimming profiles with 49-57% peak propulsive efficiency, 16-35% above standard anguilliform and carangiform references.
-
Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
The paper introduces an anchor-based heteroscedastic noise model for PBO that maps user uncertainty via KDE on reliable examples, incorporates it into GP surrogates, and derives risk-averse acquisition functions including a risk-adjusted EUBO variant that preserves one-step Bayes-optimality up to an
-
ADKO: Agentic Decentralized Knowledge Optimization
ADKO is a decentralized framework where agents share compact GP-derived tokens and LM insights to achieve collaborative Bayesian optimization with a decomposed regret bound that includes compression and approximation losses.
-
Estimating Decision Uncertainty from Preference Uncertainty: Application to Ground Vehicle Design
Preference uncertainty is modeled as random variables that induce a distribution over Pareto-optimal designs, analyzed via Sobol' indices, Shapley values, and Fréchet variance to assess decision stability in ground vehicle design.
-
Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback
VPL learns individualized vibrotactile preferences efficiently via uncertainty-aware Gaussian process models and active query selection in a 13-participant user study on an Xbox controller.
-
Stein Variational Black-Box Combinatorial Optimization
Integrating Stein variational gradient descent into EDAs introduces repulsion among particles to jointly explore multiple optima in discrete black-box optimization, with competitive or superior results on large-scale problems.
-
Neural Global Optimization via Iterative Refinement from Noisy Samples
A neural model learns iterative refinement from noisy samples and spline inputs to find global minima, reporting 8.05% mean error on multi-modal tests versus 36.24% for spline initialization alone.
-
Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise
A new ensemble Gaussian process method enforces physical constraints via weighted penalties and quadrature averaging to model noisy quantum many-body data more accurately than standard GPs.
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Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use
Trust calibration in agentic tool use is cast as preferential Bayesian optimization over a latent human risk-tolerance function observed through binary approve/deny feedback with a probit likelihood.
-
Adaptive Compression-based Lifelong Learning
Bayesian optimization enables adaptive network pruning rates in lifelong learning, performing heavier pruning on small/simple tasks and milder on large/complex ones.
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Accelerating Experimental Design by Incorporating Experimenter Hunches
A two-stage GP approach with virtual samples and posterior adjustment factors incorporates per-variable monotonic hunches into Bayesian optimization while preserving convergence guarantees, showing faster convergence on simulations and real polymer/scaffolding tasks.
-
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
Adaptive GLM with MQLE and GP regression with UCB for dynamic insurance pricing, showing parameter convergence and regret analysis under delayed claims.
-
Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference
A multi-objective Bayesian optimization framework co-optimizes CIM crossbar hardware and DNN parameters for VGG8/CIFAR-10 and VGG16/Tiny-ImageNet, achieving comparable accuracy with up to 65% smaller area and 52% lower energy.
-
Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
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Optimization of a cosmic muon tomography scanner for cargo border control inspection
Optimization study for muon tomography cargo scanner using TomOpt Bayesian optimization and GEANT4 simulations to enhance border security detection.
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Bayesian Optimization with Directionally Constrained Search
Introduces directionally constrained Bayesian optimization combining local and global search to improve optimum finding within evaluation limits.
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A Tutorial on Bayesian Optimization
Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.
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Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects
Synthetic simulations show noise hurts needle-in-haystack optimization far more than smooth landscapes with local optima, and prior domain knowledge of noise and structure is needed for effective BO in materials research.
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Automated Machine Learning in Practice: State of the Art and Recent Results
Survey of AutoML methods with benchmarks on their performance for business applications.
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Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation
Applies sequential analysis and probabilistic modeling to derive stopping rules and performance measures for policy adoption in mission-critical learning environments.
- Finite-Time Regret Analysis of Retry-Aware Bandits
- Training Language Models to Use Prolog as a Tool