Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Temperature Distribution Prediction in Laser Powder Bed Fusion using Transferable and Scalable Graph Neural Networks
This study presents novel predictive models using Graph Neural Networks (GNNs) for simulating thermal dynamics in Laser Powder Bed Fusion (L-PBF) processes. By developing and validating Single-Laser GNN (SL-GNN) and Mult…
Bayesian OptimizationFeature EngineeringAutomated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligence
Extreme edge-AI systems, such as those in readout ASICs for radiation detection, must operate under stringent hardware constraints such as micron-level dimensions, sub-milliwatt power, and nanosecond-scale speed while pr…
Bayesian OptimizationTransductive Active Learning with Application to Safe Bayesian Optimization
Safe Bayesian optimization (Safe BO) is the task of learning an optimal policy within an unknown environment, while ensuring that safety constraints are not violated. We analyze Safe BO under the lens of a generalization…
Active LearningBayesian OptimizationPredictionReinforcement Learning (RL)+3Distribution System Reconfiguration to Mitigate Load Altering Attacks via Stackelberg Games
The integration of IoT-controllable devices in power systems (such as smart electric vehicle charging stations, heat pumps, etc.), despite their benefits, raises novel cybersecurity concerns. Vulnerabilities in these dev…
Bayesian OptimizationAutomated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible. BO has been successfully applied to au…
Bayesian OptimizationHyperparameter OptimizationAugmented Bayesian Policy Search
Deterministic policies are often preferred over stochastic ones when implemented on physical systems. They can prevent erratic and harmful behaviors while being easier to implement and interpret. However, in practice, ex…
Bayesian OptimizationLEMMAPolicy Gradient Methodsreinforcement-learning+1Uncertainty-Guided Optimization on Large Language Model Search Trees
Tree search algorithms such as greedy and beam search are the standard when it comes to finding sequences of maximum likelihood in the decoding processes of large language models (LLMs). However, they are myopic since th…
Bayesian OptimizationEfficient ExplorationLanguage ModelingLanguage Modelling+1Terrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data
Terrain Classification is an essential task in space exploration, where unpredictable environments are difficult to observe using only exteroceptive sensors such as vision. Implementing Neural Network classifiers can hav…
Bayesian OptimizationClassificationHyperparameter OptimizationTime Series+1It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
In this paper, we introduce a novel approach for addressing the multi-objective optimization problem in large language model merging via black-box multi-objective optimization algorithms. The goal of model merging is to …
Bayesian OptimizationLanguage ModellingLarge Language ModelCost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite resources, it is desirable to explicitly i…
Bayesian OptimizationIn-situ Controller Autotuning by Bayesian Optimization for Closed-loop Feedback Control of Laser Powder Bed Fusion Process
Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the production of complex, high-criticality parts for various industries. This method relies on static parameter sets from exten…
Bayesian OptimizationADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models
Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine learning based optimization strategy, has…
Bayesian OptimizationIn-Context LearningModularity Based Community Detection in Hypergraphs
In this paper, we propose a scalable community detection algorithm using hypergraph modularity function, h-Louvain. It is an adaptation of the classical Louvain algorithm in the context of hypergraphs. We observe that a …
Bayesian OptimizationCommunity DetectionConvolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)
DESI is a groundbreaking international project to observe more than 40 million quasars and galaxies over a 5-year period to create a 3D map of the sky. This map will enable us to probe multiple aspects of cosmology, from…
Bayesian OptimizationData AugmentationTransfer LearningCATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization
Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of st…
Bayesian OptimizationBenchmarkingCompiler Optimizationtensor algebraAnvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool
In this work, we introduce an open-source integrated CAD-CFD tool, Anvil, which combines FreeCAD for CAD modeling and OpenFOAM for CFD analysis, along with an AI-based optimization method (Bayesian optimization) and othe…
Bayesian OptimizationPessimistic asynchronous sampling in high-cost Bayesian optimization
Asynchronous Bayesian optimization is a recently implemented technique that allows for parallel operation of experimental systems and disjointed workflows. Contrasting with serial Bayesian optimization which individually…
Bayesian OptimizationPreferential Multi-Objective Bayesian Optimization
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's latent preferences over available design choices. While preferences often involve multiple conflicting objectives, existing work i…
Autonomous DrivingBayesian OptimizationThompson SamplingTowards Bayesian Data Selection
A wide range of machine learning algorithms iteratively add data to the training sample. Examples include semi-supervised learning, active learning, multi-armed bandits, and Bayesian optimization. We embed this kind of d…
Active LearningAdditive modelsBayesian OptimizationMulti-Armed BanditsSCORE: A 1D Reparameterization Technique to Break Bayesian Optimization's Curse of Dimensionality
Bayesian optimization (BO) has emerged as a powerful tool for navigating complex search spaces, showcasing practical applications in the fields of science and engineering.However, since it typically relies on a surrogate…
Bayesian Optimization