Papers Bayesian Optimization
“Bayesian Optimization” 태그가 달린 논문 1,900편 · 필터 해제
Merge Kernel for Bayesian Optimization on Permutation Space
Bayesian Optimization (BO) algorithm is a standard tool for black-box optimization problems. The current state-of-the-art BO approach for permutation spaces relies on the Mallows kernel-an $\Omega(n^2)$ representation th…
Bayesian OptimizationLightweight Federated Learning over Wireless Edge Networks
With the exponential growth of smart devices connected to wireless networks, data production is increasing rapidly, requiring machine learning (ML) techniques to unlock its value. However, the centralized ML paradigm rai…
Bayesian OptimizationFederated LearningQuantizationBlind Targeting: Personalization under Third-Party Privacy Constraints
Major advertising platforms recently increased privacy protections by limiting advertisers' access to individual-level data. Instead of providing access to granular raw data, the platforms only allow a limited number of …
Bayesian OptimizationPrivacy PreservingReinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
Traditional wind farm control operates each turbine independently to maximize individual power output. However, coordinated wake steering across the entire farm can substantially increase the combined wind farm energy pr…
Bayesian OptimizationReinforcement Learning (RL)Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization
Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imagin…
Active LearningBayesian OptimizationData AugmentationFeasibility-Driven Trust Region Bayesian Optimization
Bayesian optimization is a powerful tool for solving real-world optimization tasks under tight evaluation budgets, making it well-suited for applications involving costly simulations or experiments. However, many of thes…
Bayesian OptimizationFast Bayesian Optimization of Function Networks with Partial Evaluations
Bayesian optimization of function networks (BOFN) is a framework for optimizing expensive-to-evaluate objective functions structured as networks, where some nodes' outputs serve as inputs for others. Many real-world appl…
Bayesian OptimizationDrug DiscoveryBayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
Bayesian optimization (BO) is a widely used iterative algorithm for optimizing black-box functions. Each iteration requires maximizing an acquisition function, such as the upper confidence bound (UCB) or a sample path fr…
Bayesian OptimizationThompson SamplingThe Gittins Index: A Design Principle for Decision-Making Under Uncertainty
The Gittins index is a tool that optimally solves a variety of decision-making problems involving uncertainty, including multi-armed bandit problems, minimizing mean latency in queues, and search problems like the Pandor…
Bayesian OptimizationDecision MakingDecision Making Under UncertaintyORFS-agent: Tool-Using Agents for Chip Design Optimization
Machine learning has been widely used to optimize complex engineering workflows across numerous domains. In the context of integrated circuit design, modern flows (e.g., going from a register-transfer level netlist to ph…
Bayesian OptimizationEfficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, making the process inefficient. We propose a …
Bayesian OptimizationTransfer LearningASPO: Constraint-Aware Bayesian Optimization for FPGA-based Soft Processors
Bayesian Optimization (BO) has shown promise in tuning processor design parameters. However, standard BO does not support constraints involving categorical parameters such as types of branch predictors and division circu…
Bayesian OptimizationDistributional encoding for Gaussian process regression with qualitative inputs
Gaussian Process (GP) regression is a popular and sample-efficient approach for many engineering applications, where observations are expensive to acquire, and is also a central ingredient of Bayesian optimization (BO), …
Bayesian OptimizationMulti-Task LearningregressionAmortized variational transdimensional inference
The expressiveness of flow-based models combined with stochastic variational inference (SVI) has, in recent years, expanded the application of optimization-based Bayesian inference to include problems with complex data r…
Bayesian InferenceBayesian OptimizationVariational InferenceActive Illumination Control in Low-Light Environments using NightHawk
Subterranean environments such as culverts present significant challenges to robot vision due to dim lighting and lack of distinctive features. Although onboard illumination can help, it introduces issues such as specula…
Bayesian OptimizationVirnyFlow: A Design Space for Responsible Model Development
Developing machine learning (ML) models requires a deep understanding of real-world problems, which are inherently multi-objective. In this paper, we present VirnyFlow, the first design space for responsible model develo…
AutoMLBayesian OptimizationMulti-Armed BanditsTest Automation for Interactive Scenarios via Promptable Traffic Simulation
Autonomous vehicle (AV) planners must undergo rigorous evaluation before widespread deployment on public roads, particularly to assess their robustness against the uncertainty of human behaviors. While recent advancement…
Bayesian OptimizationBridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation
Hybrid quantum-classical machine learning offers a path to leverage noisy intermediate-scale quantum (NISQ) devices for drug discovery, but optimal model architectures remain unclear. We systematically optimize the quant…
Bayesian OptimizationDrug DesignDrug DiscoveryConstrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization
Bayesian Optimization, leveraging Gaussian process models, has proven to be a powerful tool for minimizing expensive-to-evaluate objective functions by efficiently exploring the search space. Extensions such as constrain…
Bayesian OptimizationGaussian ProcessesGlobal optimization of graph acquisition functions for neural architecture search
Graph Bayesian optimization (BO) has shown potential as a powerful and data-efficient tool for neural architecture search (NAS). Most existing graph BO works focus on developing graph surrogates models, i.e., metrics of …
Bayesian Optimizationglobal-optimizationNeural Architecture Search