115 citations · 220 across the 11 of their papers we have counts for
12 papers
TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning
Yury Gorishniy, Akim Kotelnikov, Ivan Rubachev +1
In deep learning for tabular data, efficient ensembles of multilayer perceptrons (MLPs) have recently emerged as effective and practical architectures. Existing methods of this kin…
Benchmarking Optimizers for MLPs in Tabular Deep Learning
Yury Gorishniy, Ivan Rubachev, Dmitrii Feoktistov +1
MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used to train tabular DL models.…
Unveiling the Role of Data Uncertainty in Tabular Deep Learning
Nikolay Kartashev, Ivan Rubachev, Artem Babenko
Recent advancements in tabular deep learning have demonstrated exceptional practical performance, yet the field often lacks a clear understanding of why these techniques actually s…
Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data
George Yakushev, Alina Shutova, Ivan Rubachev +3
Tabular foundation models are becoming increasingly popular for low-resource tabular problems. These models make up for small training datasets by pretraining on large volumes of s…
On Finetuning Tabular Foundation Models
Ivan Rubachev, Akim Kotelnikov, Nikolay Kartashev +1
Foundation models are an emerging research direction in tabular deep learning. Notably, TabPFNv2 recently claimed superior performance over traditional GBDT-based methods on small-…
TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks
Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy +1
Advances in machine learning research drive progress in real-world applications. To ensure this progress, it is important to understand the potential pitfalls on the way from a nov…
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev +3
Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the rec…
TabDDPM: Modelling Tabular Data with Diffusion Models
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev +1
Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the comp…
Revisiting Pretraining Objectives for Tabular Deep Learning
Ivan Rubachev, Artem Alekberov, Yury Gorishniy +1
Recent deep learning models for tabular data currently compete with the traditional ML models based on decision trees (GBDT). Unlike GBDT, deep models can additionally benefit from…
On Embeddings for Numerical Features in Tabular Deep Learning
Yury Gorishniy, Ivan Rubachev, Artem Babenko
Recently, Transformer-like deep architectures have shown strong performance on tabular data problems. Unlike traditional models, e.g., MLP, these architectures map scalar values of…
Label-Efficient Semantic Segmentation with Diffusion Models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov +2
Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-t…
Revisiting Deep Learning Models for Tabular Data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov +1
The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed mo…