Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Time-Series-Informed Closed-loop Learning for Sequential Decision Making and Control
Closed-loop performance of sequential decision making algorithms, such as model predictive control, depends strongly on the parameters of cost functions, models, and constraints. Bayesian optimization is a common approac…
Bayesian OptimizationDecision MakingModel Predictive ControlSequential Decision Making+1Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing
Analog computing using non-volatile memristors has emerged as a promising solution for energy-efficient deep learning. New materials, like perovskites-based memristors are recently attractive due to their cost-effectiven…
Autonomous DrivingBayesian Optimizationimage-classificationImage ClassificationCBOL-Tuner: Classifier-pruned Bayesian optimization to explore temporally structured latent spaces for particle accelerator tuning
Complex dynamical systems, such as particle accelerators, often require complicated and time-consuming tuning procedures for optimal performance. It may also be required that these procedures estimate the optimal system …
Bayesian OptimizationEfficient Explorationglobal-optimizationparameter estimationBOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings
In settings where the application of reinforcement learning (RL) requires running real-world trials, including the optimization of adaptive health interventions, the number of episodes available for learning can be sever…
Bayesian OptimizationPolicy Gradient MethodsReinforcement Learning (RL)Thompson SamplingAccelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction
Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs) process the text tokens. However, the n…
Bayesian OptimizationToken ReductionDerivation of Closed Form of Expected Improvement for Gaussian Process Trained on Log-Transformed Objective
Expected Improvement (EI) is arguably the most widely used acquisition function in Bayesian optimization. However, it is often challenging to enhance the performance with EI due to its sensitivity to numerical precision.…
Bayesian OptimizationFormSensitivityRobust Bayesian Optimization via Localized Online Conformal Prediction
Bayesian optimization (BO) is a sequential approach for optimizing black-box objective functions using zeroth-order noisy observations. In BO, Gaussian processes (GPs) are employed as probabilistic surrogate models to es…
Bayesian OptimizationConformal PredictionGaussian ProcessesPredictionFast, Precise Thompson Sampling for Bayesian Optimization
Thompson sampling (TS) has optimal regret and excellent empirical performance in multi-armed bandit problems. Yet, in Bayesian optimization, TS underperforms popular acquisition functions (e.g., EI, UCB). TS samples arms…
Bayesian OptimizationSTSThompson SamplingJoint Resource Optimization, Computation Offloading and Resource Slicing for Multi-Edge Traffic-Cognitive Networks
The evolving landscape of edge computing envisions platforms operating as dynamic intermediaries between application providers and edge servers (ESs), where task offloading is coupled with payments for computational serv…
Bayesian OptimizationEdge-computingPrivacy PreservingA Simple and Efficient Approach to Batch Bayesian Optimization
Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology. However, most of current batch approaches do not scale well with the batch size. That is,…
Bayesian OptimizationAlpha Entropy Search for New Information-based Bayesian Optimization
Bayesian optimization (BO) methods based on information theory have obtained state-of-the-art results in several tasks. These techniques heavily rely on the Kullback-Leibler (KL) divergence to compute the acquisition fun…
Bayesian OptimizationLocal Bayesian Optimization for Controller Tuning with Crash Constraints
Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method for automated tuning, applying it to lar…
Bayesian OptimizationA Parameter Adaptive Trajectory Tracking and Motion Control Framework for Autonomous Vehicle
This paper studies the trajectory tracking and motion control problems for autonomous vehicles (AVs). A parameter adaptive control framework for AVs is proposed to enhance tracking accuracy and yaw stability. While estab…
Autonomous VehiclesBayesian OptimizationGPRFine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization
This paper addresses the challenge of fine-grained alignment in Vision-and-Language Navigation (VLN) tasks, where robots navigate realistic 3D environments based on natural language instructions. Current approaches use c…
Bayesian OptimizationContrastive LearningNavigateVision and Language NavigationGASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
LLMs have shown impressive capabilities across various natural language processing tasks, yet remain vulnerable to input prompts, known as jailbreak attacks, carefully designed to bypass safety guardrails and elicit harm…
Bayesian OptimizationRed TeamingIndiscriminate Disruption of Conditional Inference on Multivariate Gaussians
The multivariate Gaussian distribution underpins myriad operations-research, decision-analytic, and machine-learning models (e.g., Bayesian optimization, Gaussian influence diagrams, and variational autoencoders). Howeve…
Bayesian OptimizationTransformer Neural Processes -- Kernel Regression
Stochastic processes model various natural phenomena from disease transmission to stock prices, but simulating and quantifying their uncertainty can be computationally challenging. For example, modeling a Gaussian Proces…
Bayesian OptimizationregressionIoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose
This study proposes the IoT-Enhanced Pose Optimization Network (IE-PONet) for high-precision 3D pose estimation and motion optimization of track and field athletes. IE-PONet integrates C3D for spatiotemporal feature extr…
3D Pose EstimationBayesian OptimizationData IntegrationKeypoint Detection+2Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines
Synchrotron radiation sources play a crucial role in fields such as materials science, biology, and chemistry. The beamline, a key subsystem of the synchrotron, modulates and directs the radiation to the sample for analy…
Bayesian OptimizationDeep Reinforcement LearningReinforcement Learning (RL)Tree Species Classification using Machine Learning and 3D Tomographic SAR -- a case study in Northern Europe
Tree species classification plays an important role in nature conservation, forest inventories, forest management, and the protection of endangered species. Over the past four decades, remote sensing technologies have be…
Bayesian OptimizationClassification