Datasets and Benchmarks

Dataset and Benchmark Track 1

Joaquin Vanschoren · Serena Yeung

Moderators: Ludwig Schmidt · Viorica Patraucean

[ Abstract ]
Tue 7 Dec midnight PST — 2 a.m. PST

Abstract:

The Datasets and Benchmarks track serves as a novel venue for high-quality publications, talks, and posters on highly valuable machine learning datasets and benchmarks, as well as a forum for discussions on how to improve dataset development. Datasets and benchmarks are crucial for the development of machine learning methods, but also require their own publishing and reviewing guidelines. For instance, datasets can often not be reviewed in a double-blind fashion, and hence full anonymization will not be required. On the other hand, they do require additional specific checks, such as a proper description of how the data was collected, whether they show intrinsic bias, and whether they will remain accessible.

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