6 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
Do You Even Need More Data? Rethinking LLM Deployment for Label-Scarce Tabular Classification with TabLens
Daria Grushina, Kseniia Kuvshinova, Alina Kostromina +3
Every tabular classification task requires sufficient labeled data. When labels are scarce, practitioners can collect more examples, encode domain expertise into features, or accep…
Tsururu: A Python-based Time Series Forecasting Strategies Library
Alina Kostromina, Kseniia Kuvshinova, Aleksandr Yugay +2
While current time series research focuses on developing new models, crucial questions of selecting an optimal approach for training such models are underexplored. Tsururu, a Pytho…
LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data
Aleksey Lapin, Igor Hromov, Stanislav Chumakov +4
AutoML has advanced in handling complex tasks using the integration of LLMs, yet its efficiency remains limited by dependence on specific underlying tools. In this paper, we introd…
Towards Foundation Time Series Model: To Synthesize Or Not To Synthesize?
Kseniia Kuvshinova, Olga Tsymboi, Alina Kostromina +2
The industry is rich in cases when we are required to make forecasting for large amounts of time series at once. However, we might be in a situation where we can not afford to trai…
LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
Anton Vakhrushev, Alexander Ryzhkov, Maxim Savchenko +3
We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that thi…