4 citations · 5 across the 9 of their papers we have counts for
11 papers
Beyond Foundation Models: PFN Priors Improve Tabular Hyperparameter Optimization
Artem Chistyakov, Renat Sergazinov, Artem Babenko
Synthetic priors have so far served one purpose: pretraining tabular foundation models. We ask whether the same priors can improve conventional tabular models, which are trained fr…
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…
SwitchTab: Switched Autoencoders Are Effective Tabular Learners
Jing Wu, Suiyao Chen, Qi Zhao +9
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or…
Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification
Renat Sergazinov, Mohammadreza Armandpour, Irina Gaynanova
Deep learning models achieve state-of-the art results in predicting blood glucose trajectories, with a wide range of architectures being proposed. However, the adaptation of such m…
A case study of glucose levels during sleep using fast function on scalar regression inference
Renat Sergazinov, Andrew Leroux, Erjia Cui +4
Continuous glucose monitors (CGMs) are increasingly used to measure blood glucose levels and provide information about the treatment and management of diabetes. Our motivating stud…
Machine learning approach to force reconstruction in photoelastic materials
Renat Sergazinov, Miroslav Kramar
Photoelastic techniques have a long tradition in both qualitative and quantitative analysis of the stresses in granular materials. Over the last two decades, computational methods…