Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning. - Nick Erickson; Qi Zhao (CoRR, 2017)

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. - Rasool Fakoor; Jonas Mueller 001; Nick Erickson; Pratik Chaudhari; Alexander J. Smola (NeurIPS, 2020)

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. - Nick Erickson; Jonas Mueller 001; Alexander Shirkov; Hang Zhang 005; Pedro Larroy; Mu Li 003; Alexander J. Smola (CoRR, 2020)

Benchmarking Multimodal AutoML for Tabular Data with Text Fields. - Xingjian Shi; Jonas Mueller 001; Nick Erickson; Mu Li 003; Alexander J. Smola (NeurIPS Datasets and Benchmarks, 2021)

Multimodal AutoML for Image, Text and Tabular Data. - Nick Erickson; Xingjian Shi; James Sharpnack; Alexander J. Smola (KDD, 2022)

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. - Oleksandr Shchur; Ali Caner Türkmen; Nick Erickson; Huibin Shen; Alexander Shirkov; Tony Hu; Bernie Wang 001 (AutoML, 2023)

RLSbench: Domain Adaptation Under Relaxed Label Shift. - Saurabh Garg; Nick Erickson; James Sharpnack; Alex Smola; Sivaraman Balakrishnan; Zachary Chase Lipton (ICML, 2023)

XTab: Cross-table Pretraining for Tabular Transformers. - Bingzhao Zhu; Xingjian Shi; Nick Erickson; Mu Li 003; George Karypis; Mahsa Shoaran (ICML, 2023)

TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. - David Salinas; Nick Erickson (AutoML, 2024)

MLZero: A Multi-Agent System for End-to-end Machine Learning Automation. - Haoyang Fang; Boran Han; Nick Erickson; Xiyuan Zhang; Su Zhou; Anirudh Dagar; Jiani Zhang 003; Ali Caner Türkmen; Cuixiong Hu; Huzefa Rangwala; Ying Nian Wu; Yuyang Wang 001; et al. (CoRR, 2025)

TabArena: A Living Benchmark for Machine Learning on Tabular Data. - Nick Erickson; Lennart Purucker; Andrej Tschalzev; David Holzmüller; Prateek Mutalik Desai; David Salinas; Frank Hutter (CoRR, 2025)