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Showing 1 to 20 of 106 for “"Bayesian Optimization"”.
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Generative Bayesian Optimization for Structured Design
Black-box optimization is a general framework for problems where our goal is to find an input that maximizes some real-valued objective function. Typically, no mathematical form is available for this objective function, and it may be expensive to evaluate, making it a black-box. Many practical …
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Bayesian optimization as a probabilistic meta-program
… to write a probabilistic meta-program for Bayesian optimization, a probabilistic meta-algorithm that can combine regression frameworks such as Gaussian processes with a broad class of parameter estimation and optimization techniques? We answer both questions affirmatively, presenting both …
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Bayesian Optimization Algorithm: From Single Level to Hierarchy
The dissertation proposes the Bayesian optimization algorithm (BOA), which uses Bayesian networks to model the promising solutions found so far and sample new candidate solutions. BOA is theoretically and empirically shown to be capable of both learning a proper decomposition of the problem and …
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Towards practical theory : Bayesian optimization and optimal exploration
… the A* algorithm and the UCT algorithm), several optimization methods (e.g., Bayesian optimization and Lipschitz optimization), and some learning algorithms (e.g., PAC-MDP algorithms). For Bayesian optimization, this work solves an open problem and achieves an exponential convergence rate. For …
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Multi-Objective Bayesian Optimization with Asynchronous Batch Selection
Multi-objective optimization problems are widespread in scientific, engineering, and design f ields, necessitating a balance of trade-offs between conflicting objectives. These objectives often represent black-box functions, which are costly and time-consuming to evaluate. Multiobjective Bayesian …
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Constrained and High-dimensional Bayesian Optimization with Transformers
This thesis advances Bayesian Optimization (BO) methodology through two novel algorithms that address critical limitations in handling constraints and high-dimensional spaces. First, we introduce a constraint-handling framework leveraging Prior-data Fitted Networks (PFNs), a foundation transformer …
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Multi-Agent LLMs for Adaptive Acquisition in Bayesian Optimization
Bayesian Optimization (BO) is widely used to optimize expensive black-box objectives by learning a probabilistic surrogate and querying new points via an acquisition function. Classic approaches, however, bind acquisition behavior tightly to the surrogate (often a GP) and a small set of fixed …
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Variations on bayesian optimization applied to numerical flow simulations
Bayesian Optimization (BO) has recently regained interest in optimization problems involving expensive black-box objective functions. Several variants have been proposed in the literature, such as including gradient and/or multi-fidelity information, and it has been extended to multi-objective …
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Automating Pareto-Optimal Experiment Design via Efficient Bayesian Optimization
Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives. The objectives are often blackbox functions whose evaluation requires time-consuming and costly experiments. Multi-objective Bayesian optimization can be used to …
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Mixed-Variable Bayesian Optimization using Prior-Data Fitted Networks
Bayesian optimization (BO) is a powerful framework for optimizing expensive blackbox functions, widely used in domains such as materials science, engineering design, and hyperparameter tuning. Traditional BO relies on Gaussian processes (GPs) as surrogate models, but GPs face limitations in …
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Constrained multi-objective Bayesian optimization of simulated moving bed chromatography
… of freedom. To overcome the challenges of optimization, dynamic simulation is generally employed as a means of empirically estimating optimal operating conditions prior to experimentation, as a better alternative to trial-and-error approaches. However, dynamic SMB models are computationally …
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KL divergence – based disagreement sampling for multi-fidelity Bayesian optimization
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Bayesian optimization with Gaussian processes: Insights from hyperspectral trait search
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01
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Bayesian optimization and Cartesian-grid simulations for artificial reef design
… species. Energy dissipation was maximized using Bayesian optimization in combination with Cartesian-grid simulations and towing tank experiments. To ensure the structure’s strength, ease of implementation, and biocompatibility, the reef structures were designed to be porous. Finally, the complete …
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Multi-Objective Bilevel Bayesian optimization for robot and behavior co-design
… robot design assessment involves costly behavior optimization and performance evaluation in multiple environments. We propose a Multi-Objective Bilevel Bayesian optimization (MO-BBO) algorithm to automate the co-design process of the robot design and behavior simultaneously. Since the behavior …
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Bayesian Optimization for Engineering Design and Quality Control of Manufacturing Systems
… surface functions are extremely challenging. Bayesian optimization is a great statistical tool, based on Bayes rule, used to optimize and model these expensive-to-evaluate functions. Bayesian optimization comprises of two important components namely, a sur- rogate model often the Gaussian …
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Water Quality Control in Distribution Systems: Bayesian Optimization & Physics-Informed Machine Learning
… models for WQ prediction, and implementing the Bayesian optimization (BO) technique for optimizing the WQ in WDSs. The first thrust of this dissertation introduces a novel BO-based framework for optimizing chlorine booster scheduling in WDSs. The proposed framework integrates BO with a …
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Scaling Bayesian optimization for engineering design : lookahead approaches and multifidelity dimension reduction
… Accordingly, whether the task is formal optimization or just design space exploration, there is often a finite budget specifying the maximum number of evaluations of the objectives and constraints allowed. Bayesian optimization (BO) has become a popular global optimization technique for …
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Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
Traditionally, experimental materials optimization has used design of experiments or intuition, combined with in-depth characterization. While these methods have obtained success over the years, they are facing increasing challenges today in the face of complex aggregated systems with larger design …
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EMG-Based Human-in-the-Loop Bayesian Optimization to Assist Hip-Centric Activities
… using surface EMG-based human-in-the-loop optimization, cutting tuning time from hours to minutes while preserving assistance quality. We show that processed EMG provides a reliable objective for rapid personalization, enabling convergence within typical clinical sessions. The research …
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