Exploiting Non-Linear Structure in Astronomical Data for Improved Statistical Inference

Exploiting Low-Dimensional Structure in Astronomical Spectra

Dimension-reduction techniques can greatly improve statistical inference in astronomy. A standard approach is to use Principal Components Analysis (PCA). In this work we apply a recently-developed technique, diffusion maps, to astronomical spectra for data parameterization and dimensionality reduction, and develop a robust, eigenmode-based framework for regression. We show how our framework provides a computationally efficient means by which to predict redshifts of galaxies, and thus could inform more expensive redshift estimators such as template cross-correlation. It also provides a natural …

Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks

In wireless networks, the rate achieved depends on factors like level of interference, hardware impairments, and channel gain. Often, instantaneous values of some of these factors can be measured, and they provide useful information about the instantaneous rate achieved. For example, higher interference implies a lower rate. In this work, we treat any such measurable quality that has a non-zero correlation with the rate achieved as side-information and study how it can be exploited to quickly learn the channel that offers higher throughput (reward). When the mean value of the side-information …

超越最终标签：利用天文光变曲线分类历史中未开发的潜力

Beyond the Final Label: Exploiting the Untapped Potential of Classification Histories in Astronomical Light Curve Analysis

提出利用分类历史及其时间演化增强光变曲线分类的框架，结合循环神经网络与注意力机制提升精度，并引入基于Wasserstein距离的新评估指标。

AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It improves on the previous state-of-the-art by typically being orders of magnitude more robust toward noise and bad data, and also by discovering many formulas that stumped previous methods. We develop a method for discovering generalized symmetries (arbitrary modularity in the computational graph of a formula) from gradient properties of a neural network fit. We use normalizing flows to generalize our symbolic regre…

Modeling Light Curves for Improved Classification

Many synoptic surveys are observing large parts of the sky multiple times. The resulting lightcurves provide a wonderful window to the dynamic nature of the universe. However, there are many significant challenges in analyzing these light curves. These include heterogeneity of the data, irregularly sampled data, missing data, censored data, known but variable measurement errors, and most importantly, the need to classify in astronomical objects in real time using these imperfect light curves. We describe a modeling-based approach using Gaussian process regression for generating critical measur…

Tellipsoid: Exploiting inter-gene correlation for improved detection of differential gene expression

Motivation: Algorithms for differential analysis of microarray data are vital to modern biomedical research. Their accuracy strongly depends on effective treatment of inter-gene correlation. Correlation is ordinarily accounted for in terms of its effect on significance cut-offs. In this paper it is shown that correlation can, in fact, be exploited {to share information across tests}, which, in turn, can increase statistical power. Results: Vastly and demonstrably improved differential analysis approaches are the result of combining identifiability (the fact that in most microarray data sets, a…