相关论文: Internal Robustness: systematic search for systema…

Identifying the parameters of a model and rating competitive models based on measured data has been among the most important but challenging topics in modern science and engineering, with great potential of application in structural system…

A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modelling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often…

We introduce Bayesian Estimation Applied to Multiple Species (BEAMS), an algorithm designed to deal with parameter estimation when using contaminated data. We present the algorithm and demonstrate how it works with the help of a Gaussian…

An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through…

Currently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for automatic bin-width optimization of multidimensional histograms has been developed and recently…

We propose a robust multi-fidelity Gaussian process for integrating sparse, high-quality reference monitors with dense but noisy citizen-science sensors. The approach replaces the Gaussian log-likelihood in the high-fidelity channel with a…

We examine the utility of very high redshift Type Ia supernovae for cosmology and systematic uncertainty control. Next generation space surveys such as the Supernova/Acceleration Probe (SNAP) will obtain thousands of supernovae at z>1.7,…

Data poisoning is a training-time attack that undermines the trustworthiness of learned models. In a targeted data poisoning attack, an adversary manipulates the training dataset to alter the classification of a targeted test point. Given…

Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications. Existing causal discovery methods assume data sufficiency, which may not be the case in many real world…

We study the problem of robustly estimating the posterior distribution for the setting where observed data can be contaminated with potentially adversarial outliers. We propose Rob-ULA, a robust variant of the Unadjusted Langevin Algorithm…

Type Ia Supernovae (SNe Ia) are used as reliable cosmic distance indicators and their standardization is necessary for a more accurate measurement of the cosmological parameters of the Universe. However, the Hubble diagram still shows some…

We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning…

Current cosmological analyses which use Type Ia supernova (SN Ia) observations combine SN samples to expand the redshift range beyond that of a single sample and increase the overall sample size. The inhomogeneous photometric calibration…

We report constraints on a variety of non-standard cosmological models using the full 5-year photometrically-classified type Ia supernova sample from the Dark Energy Survey (DES-SN5YR). Both Akaike Information Criterion (AIC) and…

Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. While Bayesian…

Tiny systematic uncertainty caused by cosmological hypotheses is hard to be detected, not only because the present observational errors are relatively large but also because hypothesis-induced uncertainty is indistinguishable from other…

We explore the effect of contamination of intermediate redshift Type Ia supernova samples by Type Ibc supernovae. Simulating observed samples of Ia and mixed Ibc/Ia populations at a range of redshifts for an underlying cosmological…

In this work, we explore the cosmological consequences of the latest Type Ia supernova (SN Ia) data-set, Pantheon, by adopting the $wCDM$ model. The Pantheon data-set is the largest SN Ia samples till now, which contains 1048 supernovae on…

We present results obtained by applying our BAyesian HierArchical Modeling for the Analysis of Supernova cosmology (BAHAMAS) software package to the 740 spectroscopically confirmed supernovae type Ia (SNIa) from the "Joint Light-curve…

The peculiar motions of galaxies are powerful cosmological probes that trace the growth of structures and the distribution of matter in the universe, providing a means to investigate the nature of dark energy and test gravity on…