30 citations · 54 across the 27 of their papers we have counts for
33 papers
SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models
Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov
A new method called the Survival Beran-based Neural Importance Model (SurvBeNIM) is proposed. It aims to explain predictions of machine learning survival models, which are in the f…
SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator
Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov
An explanation method called SurvBeX is proposed to interpret predictions of the machine learning survival black-box models. The main idea behind the method is to use the modified…
A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints
Andrei V. Konstantinov, Lev V. Utkin
A new computationally simple method of imposing hard convex constraints on the neural network output values is proposed. The key idea behind the method is to map a vector of hidden…
Neural Attention Forests: Transformer-Based Forest Improvement
Andrei V. Konstantinov, Lev V. Utkin, Alexey A. Lukashin +1
A new approach called NAF (the Neural Attention Forest) for solving regression and classification tasks under tabular training data is proposed. The main idea behind the proposed N…
Interpretable Ensembles of Hyper-Rectangles as Base Models
Andrei V. Konstantinov, Lev V. Utkin
A new extremely simple ensemble-based model with the uniformly generated axis-parallel hyper-rectangles as base models (HRBM) is proposed. Two types of HRBMs are studied: closed re…
Multiple Instance Learning with Trainable Decision Tree Ensembles
Andrei V. Konstantinov, Lev V. Utkin
A new random forest based model for solving the Multiple Instance Learning (MIL) problem under small tabular data, called Soft Tree Ensemble MIL (STE-MIL), is proposed. A new type…