Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits
Traditional approaches for designing analog circuits are time-consuming and require significant human expertise. Existing automation efforts using methods like Bayesian Optimization (BO) and Reinforcement Learning (RL) a…
Bayesian OptimizationReinforcement Learning (RL)Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection
Spiking Neural Networks (SNNs), inspired by the real brain’s behavior, offer an energy-efficient alternative to traditional artificial neural networks coupled with their neural joint activity, also referred to as populat…
Bayesian OptimizationFairnessFraud DetectionImproving Fraud Detection with 1D-Convolutional Spiking Neural Networks Through Bayesian Optimization
The digitalization of the banking sector has enabled an increasing number of fraudulent activities in the past years. The development of new practical solutions for fraud detection is particularly challenging since the i…
Bayesian OptimizationFairnessFraud DetectionCropCraft: Inverse Procedural Modeling for 3D Reconstruction of Crop Plants
The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, current 3D reconstruction methods fail to r…
3D ReconstructionBayesian OptimizationA Bayesian Optimization Approach to Machine Translation Reranking
Reranking a list of candidates from a machine translation system with an external scoring model and returning the highest-scoring candidate remains a simple and effective method for improving the overall output quality. …
Bayesian OptimizationMachine TranslationRerankingTranslationCollaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective
We focus on collaborative and federated black-box optimization (BBOpt), where agents optimize their heterogeneous black-box functions through collaborative sequential experimentation. From a Bayesian optimization perspec…
Bayesian OptimizationDecision MakingDescriptiveFederated Learning+1Model Fusion through Bayesian Optimization in Language Model Fine-Tuning
Fine-tuning pre-trained models for downstream tasks is a widely adopted technique known for its adaptability and reliability across various domains. Despite its conceptual simplicity, fine-tuning entails several troubles…
Bayesian OptimizationLanguage ModelingLanguage ModellingmodelAlleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization
In the field of non-invasive medical imaging, radiomic features are utilized to measure tumor characteristics. However, these features can be affected by the techniques used to discretize the images, ultimately impacting…
Bayesian OptimizationInversion-based Latent Bayesian Optimization
Latent Bayesian optimization (LBO) approaches have successfully adopted Bayesian optimization over a continuous latent space by employing an encoder-decoder architecture to address the challenge of optimization in a high…
Bayesian OptimizationDecoderRespecting the limit:Bayesian optimization with a bound on the optimal value
In many real-world optimization problems, we have prior information about what objective function values are achievable. In this paper, we study the scenario that we have either exact knowledge of the minimum value or a,…
Bayesian OptimizationHarnessing the Power of Gradient-Based Simulations for Multi-Objective Optimization in Particle Accelerators
Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, …
Bayesian OptimizationEvolutionary Algorithmsreinforcement-learningReinforcement LearningAMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG
High-performance analog and mixed-signal (AMS) circuits are mainly full-custom designed, which is time-consuming and labor-intensive. A significant portion of the effort is experience-driven, which makes the automation o…
Bayesian OptimizationHallucinationRAGReducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness
The training or fine-tuning of machine learning, vision, and language models is often implemented as a pipeline: a sequence of stages encompassing data preparation, model training and evaluation. In this paper, we exploi…
Bayesian OptimizationGPULanguage ModelingLanguage ModellingConstrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or safety concerns often impose additional …
Active LearningBayesian OptimizationDrug DiscoveryHyperparameter OptimizationConstrained composite Bayesian optimization for rational synthesis of polymeric particles
Polymeric nano- and micro-scale particles have critical roles in tackling critical healthcare and energy challenges with their miniature characteristics. However, tailoring their synthesis process to meet specific design…
Bayesian OptimizationGraph Agnostic Causal Bayesian Optimisation
We study the problem of globally optimising a target variable of an unknown causal graph on which a sequence of soft or hard interventions can be performed. The problem of optimising the target variable associated with a…
Bayesian OptimisationBayesian OptimizationTowards safe Bayesian optimization with Wiener kernel regression
Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, the performance of BO crucially relies on…
Bayesian OptimizationregressionConditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design
The rapid discovery of new chemical compounds is essential for advancing global health and developing treatments. While generative models show promise in creating novel molecules, challenges remain in ensuring the real-w…
Bayesian OptimizationTransfer Learning for Finetuning Large Language Models
As the landscape of large language models expands, efficiently finetuning for specific tasks becomes increasingly crucial. At the same time, the landscape of parameter-efficient finetuning methods rapidly expands. Conseq…
Bayesian OptimizationMeta-LearningTransfer LearningMetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration
We present MetaMetrics-MT, an innovative metric designed to evaluate machine translation (MT) tasks by aligning closely with human preferences through Bayesian optimization with Gaussian Processes. MetaMetrics-MT enhance…
Bayesian OptimizationGaussian ProcessesMachine TranslationTranslation