: A wide range of machine learning problems, including astronomical inferenceabout galaxy clusters, natural image scene classification, parametric statistical inference,and detection of potentially harmful sources of radiation, can be well-modeledas learning a function on (samples from) distributions. This thesis explores problemsin learning such functions via kernel methods, and applies the framework to yieldstate-of-the-art results in several novel settings.One major challenge with this approach is one of computational efficiency whenlearning from large numbers of distributions: the computation of typical methodsscales between quadratically and cubically, and so they are not amenable to largedatasets. As a solution, we investigate approximate embeddings into Euclideanspaces such that inner products in the embedding space approximate kernel valuesbetween the source distributions. We provide a greater understanding of the standardexisting tool for doing so on Euclidean inputs, random Fourier features. We alsopresent a new embedding for a class of information-theoretic distribution distances,and evaluate it and existing embeddings on several real-world applications.The next challenge is that the choice of distance is important for getting goodpractical performance, but how to choose a good distance for a given problem isnot obvious. We study this problem in the setting of two-sample testing, wherewe attempt to distinguish two distributions via the maximum mean divergence, andprovide a new technique for kernel choice in these settings, including the use ofkernels defined by deep learning-type models.In a related problem setting, common to physical observations, autonomoussensing, and electoral polling, we have the following challenge: when observingsamples is expensive, but we can choose where we would like to do so, how do wepick where to observe?

https://apps.dtic.mil/sti/pdfs/AD1025786.pdf

Scalable, Flexible and Active Learning on Distributions

Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evolving and, hence, contain multiple spectral components in their constituent line profiles. Here, we present a novel method that employs machine learning techniques to identify the underlying components present within observed spectral lines, before subsequently constraining the constituent profiles through single or multiple Voigt fits. Our method allows active and quiescent components present in spectra to be identified and isolated for subsequent study. Lastly, we employ a Ca II 8542 A spectral imaging dataset as a proof-of-concept study to benchmark the suitability of our code for extracting two-component atmospheric profiles that are commonly present in sunspot chromospheres. Minimisation tests are employed to validate the reliability of the results, achieving median reduced $\chi^2$ values equal to 1.03 between the observed and synthesised umbral line profiles.

/pdf/accurately-constraining-velocity-information-from-spectral-474h5vpsqs.pdf

Accurately constraining velocity information from spectral imaging observations using machine learning techniques

Determining accurate plasma Doppler (line-of-sight) velocities from spectroscopic measurements is a challenging endeavour, especially when weak chromospheric absorption lines are often rapidly evol...

/pdf/accurately-constraining-velocity-information-from-spectral-4mf4ofncqu.pdf

Accurately constraining velocity information from spectral imaging observations using machine learning techniques.

Mass absorption cross-section of black carbon (MACBC) describes the absorptive cross-section per unit mass of black carbon, and is, thus, an essential parameter to estimate the radiative forcing of black carbon. Many studies have sought to estimate MACBC from a theoretical perspective, but these studies require the knowledge of a set of aerosol properties, which are difficult and/or labor-intensive to measure. We therefore investigate the ability of seven data analytical approaches (including different multivariate regressions, support vector machine, and neural networks) in predicting MACBC for both ambient and biomass burning measurements. Our model utilizes multi-wavelength light absorption and scattering as well as the aerosol size distributions as input variables to predict MACBC across different wavelengths. We assessed the applicability of the proposed approaches in estimating MACBC using different statistical metrics (such as coefficient of determination (R2), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MACBC appropriately, but the prediction performance varies across approaches and atmospheric environments. Based on an uncertainty evaluation of our models and the empirical and theoretical approaches to predict MACBC, we preliminarily put forth support vector machine (SVM) as a recommended data analytical technique for use. We provide an operational tool built with the approaches presented in this paper to facilitate this procedure for future users.

An Exploratory Approach Using Regression and Machine Learning in the Analysis of Mass Absorption Cross Section of Black Carbon Aerosols: Model Development and Evaluation

. The Orbiting Carbon Observatory 2 (OCO-2) carries a hyperspectral A-band sensor that can obtain information about cloud geometric thickness ( H ). The OCO2CLD-LIDAR-AUX product retrieved H with the aid of collocated CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observation) lidar data to identify suitable clouds and provide a priori cloud top pressure ( Ptop ). This collocation is no longer possible, since CALIPSO's coordination flying with OCO-2 has ended, so here we introduce a new cloud flagging and a priori assignment using only OCO-2 data, restricted to ocean footprints where solar zenith angle  45 ∘ . Firstly, a multi-layer perceptron network was trained to identify liquid clouds over the ocean with sufficient optical depth ( τ>1 ) for a valid retrieval, and agreement with MODIS–CALIPSO (Moderate Resolution Imaging Spectroradiometer) is 90.0 %. Secondly, we developed a lookup table to simultaneously retrieve cloud τ , effective radius ( re ) and Ptop from A-band and CO2 band radiances, with the intention that these will act as the a priori state estimate in a future retrieval. Median Ptop difference vs. CALIPSO is 12  hPa with an inter-decile range of [ - 11 , 87 ] hPa , substantially better than the MODIS–CALIPSO range of [ - 83 , 81 ] hPa . The MODIS–OCO-2 τ difference is 0.8 [ - 3.8 , 6.9 ] , and re is - 0.3 [ - 2.8 , 2.1 ] µ m . The τ difference is due to optically
thick and horizontally heterogeneous cloud scenes. As well as an improved passive Ptop 
retrieval, this a priori information will allow for a purely OCO-2-based Bayesian retrieval of cloud
droplet number concentration ( Nd ). Finally, our cloud flagging procedure may also be
useful for future partial-column above-cloud CO2 abundance retrievals.

/pdf/a-new-orbiting-carbon-observatory-2-cloud-flagging-method-1fkzxyfiry.pdf

Kemal Eren

Papers

scikit-learn/scikit-learn: scikit-learn 0.23.2

scikit-learn/scikit-learn: Scikit-learn 0.22.1

scikit-learn/scikit-learn: scikit-learn 0.23.1

scikit-learn: 0.17.1 release tag for DOI