Search Results for author:
Found 6 papers, 1 papers with code
Robust Calibration For Improved Weather Prediction Under Distributional Shift
In this paper, we present results on improving out-of-domain weather prediction and uncertainty estimation as part of the \texttt{Shifts Challenge on Robustness and Uncertainty under Real-World Distributional Shift} challenge.
Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions
Traditional spectral energy distribution (SED) fitting techniques face uncertainties due to assumptions in star formation histories and dust attenuation curves.
deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys.
Unsupervised Domain Adaptation for Constraining Star Formation Histories
The prevalent paradigm of machine learning today is to use past observations to predict future ones.
Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope
We leverage epistemic and aleatoric uncertainties in conjunction with probabilistic generative modeling to identify candidate vent adjustments that are in-distribution (ID); for the optimal configuration for each ID sample, we predict the reduction in required observing time to achieve a fixed SNR.
Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles
An ever-looming threat to astronomical applications of machine learning is the danger of over-fitting data, also known as the `curse of dimensionality.'
