Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Bayesian-Optimized One-Step Diffusion Model with Knowledge Distillation for Real-Time 3D Human Motion Prediction
Human motion prediction is a cornerstone of human-robot collaboration (HRC), as robots need to infer the future movements of human workers based on past motion cues to proactively plan their motion, ensuring safety in cl…
Bayesian OptimizationHuman motion predictionKnowledge Distillationmotion prediction+1SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, compositi…
Active LearningBayesian Optimizationscientific discoverySafe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control
Safe learning of control policies remains challenging, both in optimal control and reinforcement learning. In this article, we consider safe learning of parametrized predictive controllers that operate with incomplete in…
Bayesian OptimizationModel Predictive ControlAutomated design of nonreciprocal thermal emitters via Bayesian optimization
Nonreciprocal thermal emitters that break Kirchhoff's law of thermal radiation promise exciting applications for thermal and energy applications. The design of the bandwidth and angular range of the nonreciprocal effect,…
Bayesian OptimizationPhysical IntuitionOptimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach
Testing controllers in safety-critical systems is vital for ensuring their safety and preventing failures. In this paper, we address the falsification problem within learning-based closed-loop control systems through sim…
Bayesian OptimizationApplying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations
Multi fidelity Bayesian optimization (MFBO) leverages experimental and or computational data of varying quality and resource cost to optimize towards desired maxima cost effectively. This approach is particularly attract…
Bayesian OptimizationWhat makes a good concept anyway ?
A good medical ontology is expected to cover its domain completely and correctly. On the other hand, large ontologies are hard to build, hard to understand, and hard to maintain. Thus, adding new concepts (often multi-wo…
Bayesian OptimizationSample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box functions. However, in settings with very few function evaluations, a successful application of BO may require transferring …
Bayesian OptimizationTransfer LearningPerceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem
Training Single-Image Super-Resolution (SISR) models using pixel-based regression losses can achieve high distortion metrics scores (e.g., PSNR and SSIM), but often results in blurry images due to insufficient recovery o…
Bayesian OptimizationImage Quality AssessmentImage RestorationImage Super-Resolution+2Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection
The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of thes…
AutoMLBayesian OptimizationFeature Engineeringfeature selection+3TASAR: Transfer-based Attack on Skeletal Action Recognition
Skeletal sequences, as well-structured representations of human behaviors, play a vital role in Human Activity Recognition (HAR). The transferability of adversarial skeletal sequences enables attacks in real-world HAR sc…
Action RecognitionActivity RecognitionAutonomous DrivingBayesian Optimization+1PACSBO: Probably approximately correct safe Bayesian optimization
Safe Bayesian optimization (BO) algorithms promise to find optimal control policies without knowing the system dynamics while at the same time guaranteeing safety with high probability. In exchange for those guarantees, …
Bayesian OptimizationRegret Analysis for Randomized Gaussian Process Upper Confidence Bound
Gaussian process upper confidence bound (GP-UCB) is a theoretically established algorithm for Bayesian optimization (BO), where we assume the objective function $f$ follows GP. One notable drawback of GP-UCB is that the …
Bayesian OptimizationDAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism
Training social event detection models through federated learning (FedSED) aims to improve participants' performance on the task. However, existing federated learning paradigms are inadequate for achieving FedSED's objec…
Bayesian OptimizationEvent DetectionFederated LearningPersonalized Federated LearningFastBO: Fast HPO and NAS with Adaptive Fidelity Identification
Hyperparameter optimization (HPO) and neural architecture search (NAS) are powerful in attaining state-of-the-art machine learning models, with Bayesian optimization (BO) standing out as a mainstream method. Extending BO…
Bayesian OptimizationHyperparameter OptimizationNeural Architecture SearchNovel ConceptsBayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems
Bayesian optimization is a sample-efficient method for solving expensive, black-box optimization problems. Stochastic programming concerns optimization under uncertainty where, typically, average performance is the quant…
Bayesian OptimizationStochastic OptimizationSafe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes
Controller tuning and optimization have been among the most fundamental problems in robotics and mechatronic systems. The traditional methodology is usually model-based, but its performance heavily relies on an accurate …
Bayesian OptimizationGaussian ProcessesBayesian optimization of atomic structures with prior probabilities from universal interatomic potentials
The optimization of atomic structures plays a pivotal role in understanding and designing materials with desired properties. However, conventional computational methods often struggle with the formidable task of navigati…
Bayesian OptimizationGaussian ProcessesOn the effectiveness of smartphone IMU sensors and Deep Learning in the detection of cardiorespiratory conditions
This research introduces an innovative method for the early screening of cardiorespiratory diseases based on an acquisition protocol, which leverages commodity smartphone's Inertial Measurement Units (IMUs) and deep lear…
Bayesian OptimizationModel SelectionSpecificityThe Vizier Gaussian Process Bandit Algorithm
Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a large-scale service. Over multiple years, …
Bayesian Optimization