Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Hybrid Reinforcement Learning Framework for Mixed-Variable Problems
Optimization problems characterized by both discrete and continuous variables are common across various disciplines, presenting unique challenges due to their complex solution landscapes and the difficulty of navigating …
Bayesian Optimizationreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1System Identification for Lithium-Ion Batteries with Nonlinear Coupled Electro-Thermal Dynamics via Bayesian Optimization
Essential to various practical applications of lithium-ion batteries is the availability of accurate equivalent circuit models. This paper presents a new coupled electro-thermal model for batteries and studies how to ext…
Bayesian Optimizationparameter estimationEfficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to input queries. Some previous methods impr…
Adversarial AttackBayesian OptimizationRobust Entropy Search for Safe Efficient Bayesian Optimization
The practical use of Bayesian Optimization (BO) in engineering applications imposes special requirements: high sampling efficiency on the one hand and finding a robust solution on the other hand. We address the case of a…
Adversarial RobustnessBayesian OptimizationA Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment
Alzheimer Disease poses a significant challenge, necessitating early detection for effective intervention. MRI is a key neuroimaging tool due to its ease of use and cost effectiveness. This study analyzes machine learnin…
Bayesian OptimizationHippocampusCost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation
In this paper, we address the problem of cost-sensitive multi-fidelity Bayesian Optimization (BO) for efficient hyperparameter optimization (HPO). Specifically, we assume a scenario where users want to early-stop the BO …
Bayesian OptimizationHyperparameter OptimizationTransfer LearningCrystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization
Generative modeling of crystal structures is significantly challenged by the complexity of input data, which constrains the ability of these models to explore and discover novel crystals. This complexity often confines d…
Bayesian OptimizationFormation EnergyBO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
This work addresses data-driven inverse optimization (IO), where the goal is to estimate unknown parameters in an optimization model from observed decisions that can be assumed to be optimal or near-optimal solutions to …
Bayesian OptimizationUncertainty QuantificationBioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities. In this paper, we introduce BioDiscoveryAgen…
AI AgentBayesian Optimizationscientific discoveryA Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs
Training machine learning models inherently involves a resource-intensive and noisy iterative learning procedure that allows epoch-wise monitoring of the model performance. However, the insights gained from the iterative…
Bayesian OptimizationDecision MakingHyperparameter OptimizationMinimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization
Local Bayesian optimization is a promising practical approach to solve the high dimensional black-box function optimization problem. Among them is the approximated gradient class of methods, which implements a strategy s…
Bayesian OptimizationGaussian ProcessesBayesian Optimization of Functions over Node Subsets in Graphs
We address the problem of optimizing over functions defined on node subsets in a graph. The optimization of such functions is often a non-trivial task given their combinatorial, black-box and expensive-to-evaluate nature…
Bayesian OptimizationCombinatorial OptimizationHeteroscedastic Preferential Bayesian Optimization with Informative Noise Distributions
Preferential Bayesian optimization (PBO) is a sample-efficient framework for learning human preferences between candidate designs. PBO classically relies on homoscedastic noise models to represent human aleatoric uncerta…
Bayesian OptimizationInformativenessThis Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization
Bayesian Optimization (BO) has proven to be very successful at optimizing a static, noisy, costly-to-evaluate black-box function $f : \mathcal{S} \to \mathbb{R}$. However, optimizing a black-box which is also a function …
Bayesian OptimizationRetrieval-Augmented Mining of Temporal Logic Specifications from Data
The integration of cyber-physical systems (CPS) into everyday life raises the critical necessity of ensuring their safety and reliability. An important step in this direction is requirement mining, i.e. inferring formall…
Bayesian OptimizationBinary ClassificationInformation RetrievalRetrievalUnlocking Profit Potential: Maximizing Returns with Bayesian Optimization of Supertrend Indicator Parameters
This paper investigates the potential of Bayesian optimization (BO) to optimize the atr multiplier and atr period -the parameters of the Supertrend indicator for maximizing trading profits across diverse stock datasets. …
Bayesian OptimizationDiscovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation
In recent years, the researches about solving partial differential equations (PDEs) based on artificial neural network have attracted considerable attention. In these researches, the neural network models are usually des…
Bayesian OptimizationControl-Aware Transmit Power Allocation for 6G In-Factory Subnetwork Control Systems
In this paper, we develop a novel power control solution for subnetworks-enabled distributed control systems in factory settings. We propose a channel-independent control-aware (CICA) policy based on the logistic model a…
Bayesian OptimizationEfficient Line Search Method Based on Regression and Uncertainty Quantification
Unconstrained optimization problems are typically solved using iterative methods, which often depend on line search techniques to determine optimal step lengths in each iteration. This paper introduces a novel line searc…
Bayesian OptimizationregressionUncertainty QuantificationParameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization
Efficient parameter identification of electrochemical models is crucial for accurate monitoring and control of lithium-ion cells. This process becomes challenging when applied to complex models that rely on a considerabl…
Bayesian OptimizationMetaheuristic Optimization