3 code implementations • 6 Nov 2023 • David Salinas, Nick Erickson
We introduce TabRepo, a new dataset of tabular model evaluations and predictions.
2 code implementations • 10 Aug 2023 • Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Yuyang Wang
We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting.
1 code implementation • 10 May 2023 • Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran
The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data.
1 code implementation • 6 Feb 2023 • Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton
Despite the emergence of principled methods for domain adaptation under label shift, their sensitivity to shifts in class conditional distributions is precariously under explored.
2 code implementations • 4 Nov 2021 • Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola
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.
Ranked #2 on Binary Classification on kickstarter
1 code implementation • ICML Workshop AutoML 2021 • Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alex Smola
We design automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text fields as well.
1 code implementation • NeurIPS 2020 • Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola
Automated machine learning (AutoML) can produce complex model ensembles by stacking, bagging, and boosting many individual models like trees, deep networks, and nearest neighbor estimators.
8 code implementations • 13 Mar 2020 • Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander Smola
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 tabular dataset such as a CSV file.
1 code implementation • 19 Jun 2017 • Nick Erickson, Qi Zhao
This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems.