相关论文: Fast shimming algorithm based on Bayesian optimiza…

We present a fast algorithm for global rigid symmetry detection with approximation guarantees. The algorithm is guaranteed to find the best approximate symmetry of a given shape, to within a user-specified threshold, with very high…

Axions have attracted attention as promising candidates for dark matter (DM). Although axions have been intensively searched for, they have not been observed yet. Recently, novel experiments to search for axion DM have been proposed that…

Bayesian optimization is highly effective for optimizing expensive-to-evaluate black-box functions, but it faces significant computational challenges due to the cubic per-iteration cost of Gaussian processes, which results in a total time…

One of the goals of current particle physics research is to obtain evidence for new physics, that is, physics beyond the Standard Model (BSM), at accelerators such as the Large Hadron Collider (LHC) at CERN. The searches for new physics are…

The axion dark matter echo technique, proposed in Ref. [1], aims to search for axion dark matter by detecting the electromagnetic echo coming from stimulated decay of ambient axion dark matter interacting with a radio-microwave outgoing…

Axion-like particles (ALPs) are hypothetical particles that serve as promising candidates for cold dark matter. Portals like inelastic axion scattering and axion propagated force have been employed to search for the upper limit of the ALPs'…

The nature of dark matter is a fundamental puzzle in modern physics. A major approach of searching for dark matter relies on detecting feeble noise in microwave cavities. However, the quantum advantages of common quantum resources such as…

Recently there has been a huge activity in the dark matter direct detection field, with the report of an excess from CoGeNT and CRESST along with the annual modulated signal of DAMA/Libra and the strong exclusion bound from XENON100. We…

Experiments aimed at detecting ultralight dark matter typically rely on resonant effects, which are sensitive to the dark matter mass that matches the resonance frequency. In this study, we investigate the nucleon couplings of ultralight…

Bayesian optimization is used in many areas of AI for the optimization of black-box processes and has achieved impressive improvements of the state of the art for a lot of applications. It intelligently explores large and complex design…

Bayesian optimization offers a flexible framework to optimize an objective function that is expensive to be evaluated. A Bayesian optimizer iteratively queries the function values on its carefully selected points. Subsequently, it makes a…

In this paper we present a new fast and accurate method for Radial Basis Function (RBF) approximation, including interpolation as a special case, which enables us to effectively find the optimal value of the RBF shape parameter. In…

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 show that this random…

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 flexibility; in…

Revealing the essence of dark matter (DM) and dark energy is essential for understanding our universe. Ultralight (rest energy $<$10 eV) bosonic particles, including pseudoscalar axions and axion-like particles (ALPs) have emerged among…

We introduce an algorithm to marginalize the likelihood for a gravitational wave signal from a quasi-circular binary merger over its extrinsic parameters, accounting for the effects of higher harmonics and spin-induced precession. The…

Data-driven optimization of sampling patterns in MRI has recently received a significant attention.Following recent observations on the combinatorial number of minimizers in off-the-grid optimization, we propose a framework to globally…

Bayesian optimization (BO) is a model-based approach to sequentially optimize expensive black-box functions, such as the validation error of a deep neural network with respect to its hyperparameters. In many real-world scenarios, the…

Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting…

Bayesian optimization (BO) offers an elegant approach for efficiently optimizing black-box functions. However, acquisition criteria demand their own challenging inner-optimization, which can induce significant overhead. Many practical BO…