Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Collaborative Bayesian Optimization via Wasserstein Barycenters
Motivated by the growing need for black-box optimization and data privacy, we introduce a collaborative Bayesian optimization (BO) framework that addresses both of these challenges. In this framework agents work collabor…
Bayesian OptimizationInterpretable Hybrid-Rule Temporal Point Processes
Temporal Point Processes (TPPs) are widely used for modeling event sequences in various medical domains, such as disease onset prediction, progression analysis, and clinical decision support. Although TPPs effectively ca…
Bayesian OptimizationMedical DiagnosisPoint ProcessesvalidAn Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to the inherent high-dimensio…
Bayesian OptimizationBO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers
Physics-informed neural networks (PINNs) is becoming a popular alternative method for solving partial differential equations (PDEs). However, they require dedicated manual modifications to the hyperparameters of the netw…
Bayesian OptimizationMulti-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applications
This work aims at developing new methodologies to optimize computational costly complex systems (e.g., aeronautical engineering systems). The proposed surrogate-based method (often called Bayesian optimization) uses adap…
Bayesian OptimizationTowards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
Bayesian optimization (BO) is a widely used method for data-driven optimization that generally relies on zeroth-order data of objective function to construct probabilistic surrogate models. These surrogates guide the exp…
Bayesian OptimizationGaussian ProcessesSurrogate-based optimization of system architectures subject to hidden constraints
The exploration of novel architectures requires physics-based simulation due to a lack of prior experience to start from, which introduces two specific challenges for optimization algorithms: evaluations become more expe…
Bayesian OptimizationBayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, in…
Bayesian OptimizationDimensionality ReductionRegularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
Bayesian optimization is an advanced tool to perform ecient global optimization It consists on enriching iteratively surrogate Kriging models of the objective and the constraints both supposed to be computationally expen…
Bayesian Optimizationglobal-optimizationMixture-of-ExpertsGradient-based Sample Selection for Faster Bayesian Optimization
Bayesian optimization (BO) is an effective technique for black-box optimization. However, its applicability is typically limited to moderate-budget problems due to the cubic complexity in computing the Gaussian process (…
Bayesian OptimizationComputational EfficiencyDiversityCatCMA with Margin: Stochastic Optimization for Continuous, Integer, and Categorical Variables
This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencies among these different types of variabl…
Bayesian OptimizationStochastic OptimizationDeep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes
Tradable credit schemes (TCS) are an increasingly studied alternative to congestion pricing, given their revenue neutrality and ability to address issues of equity through the initial credit allocation. Modeling TCS to a…
Bayesian OptimizationComputational EfficiencyDeep Reinforcement LearningReinforcement Learning (RL)+1The Power of the Pareto Front: Balancing Uncertain Rewards for Adaptive Experimentation in scanning probe microscopy
Automated experimentation has the potential to revolutionize scientific discovery, but its effectiveness depends on well-defined optimization targets, which are often uncertain or probabilistic in real-world settings. In…
Bayesian OptimizationDecision Makingscientific discoveryCRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines
Solving black-box optimization problems with Ising machines is increasingly common in materials science. However, their application to crystal structure prediction (CSP) is still ineffective due to symmetry agnostic enco…
Bayesian OptimizationGPUAI-Assisted Transport of Radioactive Ion Beams
Beams of radioactive heavy ions allow researchers to study rare and unstable atomic nuclei, shedding light into the internal structure of exotic nuclei and on how chemical elements are formed in stars. However, the extra…
Bayesian OptimizationGOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning through Bayesian Optimization
Large Language Models (LLMs) can encode complex relationships in their latent spaces, yet harnessing them for optimization under uncertainty remains challenging. We address this gap with a novel architecture that reframe…
Bayesian OptimizationContrastive LearningDecoderLeveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testing
Bayesian optimization (BO ) is an effective method for optimizing expensive-to-evaluate black-box functions. While high-dimensional problems can be particularly challenging, due to the multitude of parameter choices and …
Bayesian OptimizationFrom Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making
Discovering novel materials with desired properties is essential for driving innovation. Industry 4.0 and smart manufacturing have promised transformative advances in this area through real-time data integration and auto…
Bayesian OptimizationData IntegrationDecision MakingEfficient Exploration+1Bayesian Optimization of Robustness Measures Using Randomized GP-UCB-based Algorithms under Input Uncertainty
Bayesian optimization based on Gaussian process upper confidence bound (GP-UCB) has a theoretical guarantee for optimizing black-box functions. Black-box functions often have input uncertainty, but even in this case, GP-…
Bayesian OptimizationMulti-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects
Bayesian Optimization (BO) machine learning method is increasingly used to guide experimental optimization tasks in materials science. To emulate the large number of input variables and noise-containing results in experi…
Bayesian OptimizationExperimental DesignSensitivity