Revisiting Deep Learning Models for Tabular Data. - Yury Gorishniy; Ivan Rubachev; Valentin Khrulkov; Artem Babenko (NeurIPS, 2021)

Label-Efficient Semantic Segmentation with Diffusion Models. - Dmitry Baranchuk; Andrey Voynov; Ivan Rubachev; Valentin Khrulkov; Artem Babenko (ICLR, 2022)

On Embeddings for Numerical Features in Tabular Deep Learning. - Yury Gorishniy; Ivan Rubachev; Artem Babenko (NeurIPS, 2022)

Revisiting Pretraining Objectives for Tabular Deep Learning. - Ivan Rubachev; Artem Alekberov; Yury Gorishniy; Artem Babenko (CoRR, 2022)

TabDDPM: Modelling Tabular Data with Diffusion Models. - Akim Kotelnikov; Dmitry Baranchuk; Ivan Rubachev; Artem Babenko (ICML, 2023)

TabR: Unlocking the Power of Retrieval-Augmented Tabular Deep Learning. - Yury Gorishniy; Ivan Rubachev; Nikolay Kartashev; Daniil Shlenskii; Akim Kotelnikov; Artem Babenko (CoRR, 2023)

TabR: Tabular Deep Learning Meets Nearest Neighbors. - Yury Gorishniy; Ivan Rubachev; Nikolay Kartashev; Daniil Shlenskii; Akim Kotelnikov; Artem Babenko (ICLR, 2024)

TabReD: A Benchmark of Tabular Machine Learning in-the-Wild. - Ivan Rubachev; Nikolay Kartashev; Yury Gorishniy; Artem Babenko (CoRR, 2024)

TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks. - Ivan Rubachev; Nikolay Kartashev; Yury Gorishniy; Artem Babenko (ICLR, 2025)

On Finetuning Tabular Foundation Models. - Ivan Rubachev; Akim Kotelnikov; Nikolay Kartashev; Artem Babenko (CoRR, 2025)