First autonomous 6D phase-space tomography system at LCLS-II achieves real-time beam reconstructions every 5-10 minutes via ML control and generative analysis.
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10 Pith papers cite this work. Polarity classification is still indexing.
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PALM-Mean combines sign-aware piecewise-linear relaxations of locally important kernel terms with closed-form analytic bounds on the rest inside a reduced-space branch-and-bound framework, yielding valid lower bounds and ε-global convergence for GP posterior mean optimization.
ADKO is a decentralized framework where agents share compact GP-derived tokens and LM insights to achieve collaborative Bayesian optimization with a decomposed regret bound that includes compression and approximation losses.
Preference uncertainty is modeled as random variables that induce a distribution over Pareto-optimal designs, analyzed via Sobol' indices, Shapley values, and Fréchet variance to assess decision stability in ground vehicle design.
VPL learns individualized vibrotactile preferences efficiently via uncertainty-aware Gaussian process models and active query selection in a 13-participant user study on an Xbox controller.
Integrating Stein variational gradient descent into EDAs introduces repulsion among particles to jointly explore multiple optima in discrete black-box optimization, with competitive or superior results on large-scale problems.
A neural model learns iterative refinement from noisy samples and spline inputs to find global minima, reporting 8.05% mean error on multi-modal tests versus 36.24% for spline initialization alone.
A multi-objective Bayesian optimization framework co-optimizes CIM crossbar hardware and DNN parameters for VGG8/CIFAR-10 and VGG16/Tiny-ImageNet, achieving comparable accuracy with up to 65% smaller area and 52% lower energy.
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.
citing papers explorer
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Autonomous operation of the DIAG0 diagnostic line for 6D phase-space monitoring at LCLS-II
First autonomous 6D phase-space tomography system at LCLS-II achieves real-time beam reconstructions every 5-10 minutes via ML control and generative analysis.
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An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
PALM-Mean combines sign-aware piecewise-linear relaxations of locally important kernel terms with closed-form analytic bounds on the rest inside a reduced-space branch-and-bound framework, yielding valid lower bounds and ε-global convergence for GP posterior mean optimization.
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ADKO: Agentic Decentralized Knowledge Optimization
ADKO is a decentralized framework where agents share compact GP-derived tokens and LM insights to achieve collaborative Bayesian optimization with a decomposed regret bound that includes compression and approximation losses.
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Estimating Decision Uncertainty from Preference Uncertainty: Application to Ground Vehicle Design
Preference uncertainty is modeled as random variables that induce a distribution over Pareto-optimal designs, analyzed via Sobol' indices, Shapley values, and Fréchet variance to assess decision stability in ground vehicle design.
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Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback
VPL learns individualized vibrotactile preferences efficiently via uncertainty-aware Gaussian process models and active query selection in a 13-participant user study on an Xbox controller.
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Stein Variational Black-Box Combinatorial Optimization
Integrating Stein variational gradient descent into EDAs introduces repulsion among particles to jointly explore multiple optima in discrete black-box optimization, with competitive or superior results on large-scale problems.
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Neural Global Optimization via Iterative Refinement from Noisy Samples
A neural model learns iterative refinement from noisy samples and spline inputs to find global minima, reporting 8.05% mean error on multi-modal tests versus 36.24% for spline initialization alone.
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Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference
A multi-objective Bayesian optimization framework co-optimizes CIM crossbar hardware and DNN parameters for VGG8/CIFAR-10 and VGG16/Tiny-ImageNet, achieving comparable accuracy with up to 65% smaller area and 52% lower energy.
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Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
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A Tutorial on Bayesian Optimization
Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.