4 citations · 5 across the 7 of their papers we have counts for
9 papers
Agentic Search Spaces for Tabular Machine Learning
Renat Sergazinov, Artem Chistyakov, Sergey Pankevich +1
Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains underexplored. In this paper…
Chunked TabPFN: Exact Training-Free In-Context Learning for Long-Context Tabular Data
Renat Sergazinov, Shao-An Yin
TabPFN v2 achieves better results than tree-based models on several tabular benchmarks, which is notable since tree-based models are usually the strongest choice for tabular data.…
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…
Random at First, Fast at Last: NTK-Guided Fourier Pre-Processing for Tabular DL
Renat Sergazinov, Jing Wu, Shao-An Yin
While random Fourier features are a classic tool in kernel methods, their utility as a pre-processing step for deep learning on tabular data has been largely overlooked. Motivated…
GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks
Renat Sergazinov, Elizabeth Chun, Valeriya Rogovchenko +3
The rising rates of diabetes necessitate innovative methods for its management. Continuous glucose monitors (CGM) are small medical devices that measure blood glucose levels at reg…
A spectral method for multi-view subspace learning using the product of projections
Renat Sergazinov, Armeen Taeb, Irina Gaynanova
Multi-view data provides complementary information on the same set of observations, with multi-omics and multimodal sensor data being common examples. Analyzing such data typically…