Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators
A Bayesian optimization approach for maximizing the gas conversion rate in an industrial-scale bioreactor for syngas fermentation is presented. We have access to a high-fidelity, computational fluid dynamic (CFD) reactor…
Bayesian OptimisationBayesian OptimizationSafe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificates
Safety-critical control tasks with high levels of uncertainty are becoming increasingly common. Typically, techniques that guarantee safety during learning and control utilize constraint-based safety certificates, which …
Bayesian OptimizationSafe Sequential Optimization for Switching Environments
We consider the problem of designing a sequential decision making agent to maximize an unknown time-varying function which switches with time. At each step, the agent receives an observation of the function's value at a …
Bayesian OptimizationChange Point DetectionDecision MakingSequential Decision MakingJoint Composite Latent Space Bayesian Optimization
Bayesian Optimization (BO) is a technique for sample-efficient black-box optimization that employs probabilistic models to identify promising input locations for evaluation. When dealing with composite-structured functio…
Bayesian OptimizationBayesian Optimization of Function Networks with Partial Evaluations
Bayesian optimization is a powerful framework for optimizing functions that are expensive or time-consuming to evaluate. Recent work has considered Bayesian optimization of function networks (BOFN), where the objective f…
Bayesian OptimizationBatch Bayesian Optimization for Replicable Experimental Design
Many real-world experimental design problems (a) evaluate multiple experimental conditions in parallel and (b) replicate each condition multiple times due to large and heteroscedastic observation noise. Given a fixed tot…
AutoMLBayesian OptimizationExperimental DesignThompson SamplingUnexpected Improvements to Expected Improvement for Bayesian Optimization
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods. …
Bayesian OptimizationEfficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs
Bayesian Optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications. In some challenging BO tasks, input uncertainty arises due to the inevitable randomness in the optimiza…
Bayesian OptimizationAdvancing Bayesian Optimization via Learning Correlated Latent Space
Bayesian optimization is a powerful method for optimizing black-box functions with limited function evaluations. Recent works have shown that optimization in a latent space through deep generative models such as variatio…
Bayesian OptimizationPolyThrottle: Energy-efficient Neural Network Inference on Edge Devices
As neural networks (NN) are deployed across diverse sectors, their energy demand correspondingly grows. While several prior works have focused on reducing energy consumption during training, the continuous operation of M…
Bayesian OptimizationCPUEfficient Neural NetworkGPURIS-aided Real-time Beam Tracking for a Mobile User via Bayesian Optimization
The conventional beam management procedure mandates that the user equipment (UE) periodically measure the received signal reference power (RSRP) and transmit these measurements to the base station (BS). The challenge lie…
Bayesian OptimizationManagementSustainable Concrete via Bayesian Optimization
Eight percent of global carbon dioxide emissions can be attributed to the production of cement, the main component of concrete, which is also the dominant source of CO2 emissions in the construction of data centers. The …
Bayesian OptimizationExperimental DesignBlack-Box Optimization with Implicit Constraints for Public Policy
Black-box optimization (BBO) has become increasingly relevant for tackling complex decision-making problems, especially in public policy domains such as police redistricting. However, its broader application in public po…
Bayesian OptimizationDecision MakingLooping in the Human Collaborative and Explainable Bayesian Optimization
Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specifie…
Bayesian OptimizationAdaptive importance sampling for heavy-tailed distributions via $α$-divergence minimization
Adaptive importance sampling (AIS) algorithms are widely used to approximate expectations with respect to complicated target probability distributions. When the target has heavy tails, existing AIS algorithms can provide…
Bayesian OptimizationVariational InferenceqPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
Classical evolutionary approaches for multiobjective optimization are quite accurate but incur a lot of queries to the objectives; this can be prohibitive when objectives are expensive oracles. A sample-efficient approac…
Bayesian OptimizationComputational EfficiencyMultiobjective OptimizationThompson SamplingROM-Based Stochastic Optimization for a Continuous Manufacturing Process
This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the p…
Bayesian OptimizationStochastic OptimizationHyperparameter optimization of hp-greedy reduced basis for gravitational wave surrogates
In a previous work we introduced, in the context of gravitational wave science, an initial study on an automated domain-decomposition approach for reduced basis through hp-greedy refinement. The approach constructs local…
Bayesian OptimizationHyperparameter Optimizationparameter estimationRandom Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency
We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random samples drawn from a distribution. We s…
Bayesian OptimizationComputational EfficiencyComparative Analysis of Machine Learning Algorithms for Solar Irradiance Forecasting in Smart Grids
The increasing global demand for clean and environmentally friendly energy resources has caused increased interest in harnessing solar power through photovoltaic (PV) systems for smart grids and homes. However, the inher…
Bayesian Optimizationenergy tradingfeature selectionManagement+1