16 citations · 21 across the 3 of their papers we have counts for
4 papers
Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling
Alexey Zaytsev, Diana Koldasbayeva, Egor Serov
Kernel Mean Matching (KMM) estimates risk under covariate shift by reweighting labelled source samples so that their kernel mean embedding matches that of unlabeled target samples.…
Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
Evaluating the predictive performance of species distribution models (SDMs) under realistic deployment scenarios requires careful handling of spatial and temporal dependencies in t…
Challenges in data-based geospatial modeling for environmental research and practice
Diana Koldasbayeva, Polina Tregubova, Mikhail Gasanov +3
With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research.…
Correcting sampling biases via importance reweighting for spatial modeling
Boris Prokhorov, Diana Koldasbayeva, Alexey Zaytsev
In machine learning models, the estimation of errors is often complex due to distribution bias, particularly in spatial data such as those found in environmental studies. We introd…