Aziz Temirkhanov

Abdalaziz Rashid Al-Maeeni, Aziz Temirkhanov, Artem Ryzhikov et al.

High-precision modeling of systems is one of the main areas of industrial data analysis. Models of systems, their digital twins, are used to predict their behavior under various conditions. We have developed several models of a storage system using machine learning-based generative models. The system consists of several components: hard disk drive (HDD) and solid-state drive (SSD) storage pools with different RAID schemes and cache. Each storage component is represented by a probabilistic model that describes the probability distribution of the component performance in terms of IOPS and latency, depending on their configuration and external data load parameters. The results of the experiments demonstrate the errors of 4-10 % for IOPS and 3-16 % for latency predictions depending on the components and models of the system. The predictions show up to 0.99 Pearson correlation with Little's law, which can be used for unsupervised reliability checks of the models. In addition, we present novel data sets that can be used for benchmarking regression algorithms, conditional generative models, and uncertainty estimation methods in machine learning.

Daria Grushina, Kseniia Kuvshinova, Alina Kostromina et al.

Supervised classification for tabular data remains a core machine learning task, yet its reliance on large labeled datasets limits applicability in data-scarce domains. For such few-shot scenarios, specialized methods like TabPFN - a state-of-the-art Prior-Data Fitted Network - have set a high standard by leveraging large-scale synthetic pretraining, though they still require a context of labeled examples to function. In contrast, Large Language Models (LLMs) could offer a more flexible alternative via zero- and few-shot in-context learning directly from task descriptions, but their performance on tabular data remains inconsistent and poorly understood. We introduce LLMTabBench, a benchmark designed to systematically evaluate LLMs for tabular classification under data-scarce conditions. LLMTabBench explicitly probes (i) how LLM prior knowledge interacts with in-context information (task descriptions and few-shot examples), and (ii) how model performance scales with increasing data complexity, using both real-world and controlled synthetic datasets. Our findings include: (1) LLMs are highly competitive in zero-shot settings and can outperform alternative models, even when those models have access to few-shot examples; (2) incorporating additional few-shot examples can conflict with LLM prior knowledge, limiting or even degrading performance; and (3) there is a data complexity threshold beyond which LLMs' performance declines and few-shot examples become less effective. Together, these findings reveal fundamental constraints of in-context learning for tabular data and provide practical guidance for deploying LLMs in low-data regimes.