Papers Bayesian Optimization
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Transductive Spiking Graph Neural Networks for Loihi
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural netwo…
Bayesian OptimizationGraph ClassificationHyperparameter OptimizationPhysics-based reward driven image analysis in microscopy
The rise of electron microscopy has expanded our ability to acquire nanometer and atomically resolved images of complex materials. The resulting vast datasets are typically analyzed by human operators, an intrinsically c…
Bayesian OptimizationAdaptive Bayesian Optimization for High-Precision Motion Systems
Controller tuning and parameter optimization are crucial in system design to improve closed-loop system performance. Bayesian optimization has been established as an efficient model-free controller tuning and adaptation …
Bayesian OptimizationComputational EfficiencyMachine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials
There has been significant recent interest in the mechanics community to design structures that can either violate reciprocity, or exhibit elastic asymmetry or odd elasticity. While these properties are highly desirable …
Bayesian OptimizationKATO: Knowledge Alignment and Transfer for Transistor Sizing of Different Design and Technology
Automatic transistor sizing in circuit design continues to be a formidable challenge. Despite that Bayesian optimization (BO) has achieved significant success, it is circuit-specific, limiting the accumulation and transf…
Bayesian OptimizationTransfer LearningExpected Coordinate Improvement for High-Dimensional Bayesian Optimization
Bayesian optimization (BO) algorithm is very popular for solving low-dimensional expensive optimization problems. Extending Bayesian optimization to high dimension is a meaningful but challenging task. One of the major c…
Bayesian OptimizationA Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
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 variables…
Bayesian OptimisationBayesian OptimizationStability-informed Bayesian Optimization for MPC Cost Function Learning
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect in…
Bayesian Optimizationglobal-optimizationAdaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning
High-performance catalysts are crucial for sustainable energy conversion and human health. However, the discovery of catalysts faces challenges due to the absence of efficient approaches to navigating vast and high-dimen…
Bayesian OptimizationRepresentation LearningUncertainty QuantificationBAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification
Pre-processing whole slide images (WSIs) can impact classification performance. Our study shows that using fixed hyper-parameters for pre-processing out-of-domain WSIs can significantly degrade performance. Therefore, it…
Bayesian Optimizationwhole slide imagesVDTuner: Automated Performance Tuning for Vector Data Management Systems
Vector data management systems (VDMSs) have become an indispensable cornerstone in large-scale information retrieval and machine learning systems like large language models. To enhance the efficiency and flexibility of s…
Bayesian OptimizationInformation RetrievalManagementHuman-Algorithm Collaborative Bayesian Optimization for Engineering Systems
Bayesian optimization has been successfully applied throughout Chemical Engineering for the optimization of functions that are expensive-to-evaluate, or where gradients are not easily obtainable. However, domain experts …
Bayesian OptimizationDecision MakingIntegrating Marketing Channels into Quantile Transformation and Bayesian Optimization of Ensemble Kernels for Sales Prediction with Gaussian Process Models
This study introduces an innovative Gaussian Process (GP) model utilizing an ensemble kernel that integrates Radial Basis Function (RBF), Rational Quadratic, and Mat\'ern kernels for product sales forecasting. By applyin…
Bayesian OptimizationMarketingEnhancing Predictive Accuracy in Pharmaceutical Sales Through An Ensemble Kernel Gaussian Process Regression Approach
This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Mat\'ern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimizati…
Bayesian OptimizationGPRLearning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging
Tuning parameters in model predictive control (MPC) presents significant challenges, particularly when there is a notable discrepancy between the controller's predictions and the actual behavior of the closed-loop plant.…
Bayesian OptimizationModel Predictive ControlHyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
SecureBoost is a tree-boosting algorithm that leverages homomorphic encryption (HE) to protect data privacy in vertical federated learning. SecureBoost and its variants have been widely adopted in fields such as finance …
Bayesian OptimizationFederated LearningHyperparameter OptimizationInference Attack+1Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks
Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce NEON (Neural Epistemic Ope…
Bayesian OptimizationDecision MakingOperator learningSequential Decision MakingEfficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning
AutoMPC is a Python package that automates and optimizes data-driven model predictive control. However, it can be computationally expensive and unstable when exploring large search spaces using pure Bayesian Optimization…
Bayesian OptimizationMeta-LearningModel Predictive ControlCheckpoint Merging via Bayesian Optimization in LLM Pretraining
The rapid proliferation of large language models (LLMs) such as GPT-4 and Gemini underscores the intense demand for resources during their training processes, posing significant challenges due to substantial computationa…
Bayesian OptimizationBLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge…
Bayesian Optimization