157 citations · 200 across the 7 of their papers we have counts for
7 papers
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
Oleksandr Shchur, Caner Turkmen, Nick Erickson +4
We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables use…
XTab: Cross-table Pretraining for Tabular Transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson +3
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-su…
RLSbench: Domain Adaptation Under Relaxed Label Shift
Saurabh Garg, Nick Erickson, James Sharpnack +3
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored.…
Benchmarking Multimodal AutoML for Tabular Data with Text Fields
Xingjian Shi, Jonas Mueller, Nick Erickson +2
We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well. Here we assembl…
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Rasool Fakoor, Jonas Mueller, Nick Erickson +2
Automated machine learning (AutoML) can produce complex model ensembles by stacking, bagging, and boosting many individual models like trees, deep networks, and nearest neighbor es…
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Nick Erickson, Jonas Mueller, Alexander Shirkov +4
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabula…