# AutoML @ NeurIPS 2018 challenge: Design and Results

arXiv: https://arxiv.org/abs/1903.05263 · HTML: https://mlsanity.com/paper/1903.05263/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://mlsanity.com/paper/2111.02705/) (2021)
- [Fraud Dataset Benchmark and Applications](https://mlsanity.com/paper/2208.14417/) (2022)
- [3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding](https://mlsanity.com/paper/2212.06833/) (2022)
- [Academic competitions](https://mlsanity.com/paper/2312.00268/) (2023)

## Abstract (arXiv)

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.