Ivan Rubachev

I work on improving deep learning for tabular data.

Nikolay Kartashev

Yury Gorishniy

Artem Babenko

TabReD is a benchmark for evaluating tabular machine learning models under conditions representative of real-world deployments. It comprises eight datasets from production ML systems at Yandex and Kaggle competitions. TabReD addresses two gaps in existing benchmarks: (1) all datasets use time-based train/validation/test splits to evaluate models under temporal distribution drift, and (2) datasets are feature-rich (median 261 features vs. 13-23 in prior benchmarks) with extensive feature engineering, reflecting real ML pipelines. Experiments on TabReD demonstrate that methods successful on standard benchmarks may underperform on TabReD, making it a critical testbed for assessing whether tabular ML approaches generalize to industrial settings.

Tabular data involves two-dimensional tables with objects (rows) and features (columns), which are used in numerous applied tasks such as classification, regression, ranking and many others.