Nick Erickson

According to our database, Nick Erickson authored at least 9 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications.

David Salinas, Nick Erickson

CoRR, 2023

XTab: Cross-table Pretraining for Tabular Transformers.

Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran

Proceedings of the International Conference on Machine Learning, 2023

RLSbench: Domain Adaptation Under Relaxed Label Shift.

Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton

Proceedings of the International Conference on Machine Learning, 2023

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting.

Oleksandr Shchur, Ali Caner Türkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, Bernie Wang

Proceedings of the International Conference on Automated Machine Learning, 2023

2022

Multimodal AutoML for Image, Text and Tabular Data.

Nick Erickson, Xingjian Shi, James Sharpnack, Alexander J. Smola

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Benchmarking Multimodal AutoML for Tabular Data with Text Fields.

Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.

Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander J. Smola

CoRR, 2020

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

Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2017

Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning.

Nick Erickson, Qi Zhao

CoRR, 2017