Search Results for author: Nick Erickson

Found 5 papers, 5 papers with code

Benchmarking Multimodal AutoML for Tabular Data with Text Fields

1 code implementation4 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.

AutoML

Multimodal AutoML on Structured Tables with Text Fields

2 code implementations 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.

AutoML

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation

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.

AutoML Data Augmentation

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

7 code implementations13 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.

Neural Architecture Search

Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning

1 code implementation19 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.

Continual Learning General Reinforcement Learning +2

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