Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Cooperative Bayesian Optimization for Imperfect Agents
We introduce a cooperative Bayesian optimization problem for optimizing black-box functions of two variables where two agents choose together at which points to query the function but have only control over one variable …
Bayesian OptimizationDecision MakingSequential Decision MakingTS-RSR: A provably efficient approach for batch Bayesian Optimization
This paper presents a new approach for batch Bayesian Optimization (BO) called Thompson Sampling-Regret to Sigma Ratio directed sampling (TS-RSR), where we sample a new batch of actions by minimizing a Thompson Sampling …
Bayesian OptimizationThompson SamplingController Adaptation via Learning Solutions of Contextual Bayesian Optimization
In this work, we propose a framework for adapting the controller's parameters based on learning optimal solutions from contextual black-box optimization problems. We consider a class of control design problems for dynami…
Bayesian OptimizationGaussian ProcessesModel Predictive ControlTargeted Variance Reduction: Robust Bayesian Optimization of Black-Box Simulators with Noise Parameters
The optimization of a black-box simulator over control parameters $\mathbf{x}$ arises in a myriad of scientific applications. In such applications, the simulator often takes the form $f(\mathbf{x},\boldsymbol{\theta})$, …
Bayesian OptimizationRobust DesignParallel Hyperparameter Optimization Of Spiking Neural Network
Spiking Neural Networks (SNN). SNNs are based on a more biologically inspired approach than usual artificial neural networks. Such models are characterized by complex dynamics between neurons and spikes. These are very s…
Bayesian OptimizationGPUHyperparameter OptimizationNeural Architecture SearchEpsilon-Greedy Thompson Sampling to Bayesian Optimization
Bayesian optimization (BO) has become a powerful tool for solving simulation-based engineering optimization problems thanks to its ability to integrate physical and mathematical understandings, consider uncertainty, and …
Bayesian OptimizationCantilever BeamThompson SamplingTowards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Laser-directed-energy deposition (DED) offers advantages in additive manufacturing (AM) for creating intricate geometries and material grading. Yet, challenges like material inconsistency and part variability remain, mai…
Bayesian InferenceBayesian OptimizationTime SeriesTransfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays
With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, som…
Bayesian OptimizationDiagnosticTransfer LearningEnhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design proce…
Bayesian OptimizationExperimental DesignStopping Bayesian Optimization with Probabilistic Regret Bounds
Bayesian optimization is a popular framework for efficiently tackling black-box search problems. As a rule, these algorithms operate by iteratively choosing what to evaluate next until some predefined budget has been exh…
Bayesian OptimizationConcurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the crucial challenge of ensuring safet…
Bayesian OptimizationBilevel OptimizationDecision MakingReinforcement Learning (RL)Information-Theoretic Safe Bayesian Optimization
We consider a sequential decision making task, where the goal is to optimize an unknown function without evaluating parameters that violate an a~priori unknown (safety) constraint. A common approach is to place a Gaussia…
Bayesian OptimizationDecision MakingSafe ExplorationSequential Decision MakingGlobal Safe Sequential Learning via Efficient Knowledge Transfer
Sequential learning methods, such as active learning and Bayesian optimization, aim to select the most informative data for task learning. In many applications, however, data selection is constrained by unknown safety co…
Active LearningBayesian OptimizationComputational EfficiencyGaussian Processes+1FlexHB: a More Efficient and Flexible Framework for Hyperparameter Optimization
Given a Hyperparameter Optimization(HPO) problem, how to design an algorithm to find optimal configurations efficiently? Bayesian Optimization(BO) and the multi-fidelity BO methods employ surrogate models to sample confi…
Bayesian OptimizationHyperparameter OptimizationPI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information
Physics informed neural networks (PINNs) have recently been proposed as surrogate models for solving process optimization problems. However, in an active learning setting collecting enough data for reliably training PINN…
Active LearningBayesian OptimizationBilevel OptimizationA Systematic Review of Low-Rank and Local Low-Rank Matrix Approximation in Big Data Medical Imaging
The large volume and complexity of medical imaging datasets are bottlenecks for storage, transmission, and processing. To tackle these challenges, the application of low-rank matrix approximation (LRMA) and its derivativ…
Bayesian OptimizationImage SegmentationMissing ValuesSemantic SegmentationTowards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often non-differentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interacti…
Active LearningBayesian OptimizationVariational Entropy Search for Adjusting Expected Improvement
Bayesian optimization is a widely used technique for optimizing black-box functions, with Expected Improvement (EI) being the most commonly utilized acquisition function in this domain. While EI is often viewed as distin…
Bayesian OptimizationVariational InferencePhysics-based material parameters extraction from perovskite experiments via Bayesian optimization
The ability to extract material parameters of perovskite from quantitative experimental analysis is essential for rational design of photovoltaic and optoelectronic applications. However, the difficulty of this analysis …
Bayesian OptimizationTransition Constrained Bayesian Optimization via Markov Decision Processes
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many real-life problems do not offer this flexib…
Bayesian Optimization