A Deep Forest Improvement by Using Weighted Schemes. - Lev V. Utkin; Andrei V. Konstantinov; Anna A. Meldo; Mikhail A. Ryabinin; Viacheslav S. Chukanov (FRUCT, 2019)

An Adaptive Weighted Deep Forest Classifier. - Lev V. Utkin; Andrei V. Konstantinov; Viacheslav S. Chukanov; Mikhail V. Kots; Anna A. Meldo (CoRR, 2019)

A weighted random survival forest. - Lev V. Utkin; Andrei V. Konstantinov; Viacheslav S. Chukanov; Mikhail V. Kots; Mikhail A. Ryabinin; Anna A. Meldo (Knowl. Based Syst., 2019)

Gradient boosting machine with partially randomized decision trees. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2020)

A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2020)

Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors. - Lev V. Utkin; Mikhail V. Kots; Viacheslav S. Chukanov; Andrei V. Konstantinov; Anna A. Meldo (Int. J. Artif. Intell. Tools, 2020)

A New Adaptive Weighted Deep Forest and Its Modifications. - Lev V. Utkin; Andrei V. Konstantinov; Viacheslav S. Chukanov; Anna A. Meldo (Int. J. Inf. Technol. Decis. Mak., 2020)

Gradient Boosting Machine with Partially Randomized Decision Trees. - Andrei V. Konstantinov; Lev V. Utkin; Vladimir Muliukha (FRUCT, 2021)

Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions. - Lev V. Utkin; Pavel D. Drobintsev; Maxim Kovalev; Andrei V. Konstantinov (FRUCT, 2021)

Uncertainty Interpretation of the Machine Learning Survival Model Predictions. - Lev V. Utkin; Vladimir S. Zaborovsky; Maxim S. Kovalev; Andrei V. Konstantinov; Natalia A. Politaeva; Alexey Lukashin (IEEE Access, 2021)

An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data. - Lev V. Utkin; Andrei V. Konstantinov; Kirill A. Vishniakov (CoRR, 2021)

Counterfactual Explanation of Machine Learning Survival Models. - Maxim Kovalev; Lev V. Utkin; Frank P. A. Coolen; Andrei V. Konstantinov (Informatica, 2021)

Interpretable machine learning with an ensemble of gradient boosting machines. - Andrei V. Konstantinov; Lev V. Utkin (Knowl. Based Syst., 2021)

Multiple Instance Learning through Explanation by Using a Histopathology Example. - Andrei V. Konstantinov; Lev V. Utkin (FRUCT, 2022)

AGBoost: Attention-based Modification of Gradient Boosting Machine. - Andrei V. Konstantinov; Lev V. Utkin; Stanislav Kirpichenko (FRUCT, 2022)

Ensembles of Random SHAPs. - Lev V. Utkin; Andrei V. Konstantinov (Algorithms, 2022)

Attention and Self-Attention in Random Forests. - Lev V. Utkin; Andrei V. Konstantinov (CoRR, 2022)

Improved Anomaly Detection by Using the Attention-Based Isolation Forest. - Lev V. Utkin; Andrey Y. Ageev; Andrei V. Konstantinov (CoRR, 2022)

LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2022)

BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect. - Stanislav R. Kirpichenko; Lev V. Utkin; Andrei V. Konstantinov (CoRR, 2022)

Multi-attention multiple instance learning. - Andrei V. Konstantinov; Lev V. Utkin (Neural Comput. Appl., 2022)

Attention-based random forest and contamination model. - Lev V. Utkin; Andrei V. Konstantinov (Neural Networks, 2022)

SurvNAM: The machine learning survival model explanation. - Lev V. Utkin; Egor D. Satyukov; Andrei V. Konstantinov (Neural Networks, 2022)

GBMILs: Gradient Boosting Models for Multiple Instance Learning. - Andrei V. Konstantinov; Lev V. Utkin; Vladimir Muliukha; Vladimir S. Zaborovsky (ICR, 2023)

Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression. - Andrei V. Konstantinov; Stanislav Kirpichenko; Lev V. Utkin (Algorithms, 2023)

Multiple Instance Learning with Trainable Soft Decision Tree Ensembles. - Andrei V. Konstantinov; Lev V. Utkin; Vladimir Muliukha (Algorithms, 2023)

Multiple Instance Learning with Trainable Decision Tree Ensembles. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2023)

Neural Attention Forests: Transformer-Based Forest Improvement. - Andrei V. Konstantinov; Lev V. Utkin; Alexey Lukashin; Vladimir Muliukha (CoRR, 2023)

A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2023)

SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator. - Lev V. Utkin; Danila Y. Eremenko; Andrei V. Konstantinov (CoRR, 2023)

SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models. - Lev V. Utkin; Danila Y. Eremenko; Andrei V. Konstantinov (CoRR, 2023)

LARF: Two-Level Attention-Based Random Forests with a Mixture of Contamination Models. - Andrei V. Konstantinov; Lev V. Utkin; Vladimir Muliukha (Informatics, 2023)

Improved Anomaly Detection by Using the Attention-Based Isolation Forest. - Lev V. Utkin; Andrey Y. Ageev; Andrei V. Konstantinov; Vladimir Muliukha (Algorithms, 2023)

Attention-like feature explanation for tabular data. - Andrei V. Konstantinov; Lev V. Utkin (Int. J. Data Sci. Anal., 2023)

Attention and self-attention in random forests. - Lev V. Utkin; Andrei V. Konstantinov; Stanislav R. Kirpichenko (Prog. Artif. Intell., 2023)

Interpretable ensembles of hyper-rectangles as base models. - Andrei V. Konstantinov; Lev V. Utkin (Neural Comput. Appl., 2023)

BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect. - Stanislav Kirpichenko; Lev V. Utkin; Andrei V. Konstantinov; Vladimir Muliukha (Algorithms, 2024)

Dual feature-based and example-based explanation methods. - Andrei V. Konstantinov; Boris V. Kozlov; Stanislav R. Kirpichenko; Lev V. Utkin (CoRR, 2024)

Generating Survival Interpretable Trajectories and Data. - Andrei V. Konstantinov; Stanislav R. Kirpichenko; Lev V. Utkin (CoRR, 2024)

Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2024)

FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules. - Lev V. Utkin; Andrei V. Konstantinov; Stanislav R. Kirpichenko (CoRR, 2024)

SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms. - Lev V. Utkin; Semen P. Khomets; Vlada A. Efremenko; Andrei V. Konstantinov (CoRR, 2024)

SurvBeNIM: The Beran-Based Neural Importance Model for Explaining Survival Models. - Lev V. Utkin; Danila Y. Eremenko; Andrei V. Konstantinov; Vladimir Mulukha (IEEE Access, 2025)

Dual feature-based and example-based explanation methods. - Andrei V. Konstantinov; Boris V. Kozlov; Stanislav Kirpichenko; Lev V. Utkin; Vladimir Muliukha (Frontiers Artif. Intell., 2025)

Survival Concept-Based Learning Models. - Stanislav R. Kirpichenko; Lev V. Utkin; Andrei V. Konstantinov; Natalya M. Verbova (CoRR, 2025)

A novel gradient-based method for decision trees optimizing arbitrary differential loss functions. - Andrei V. Konstantinov; Lev V. Utkin (CoRR, 2025)

Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions. - Lev V. Utkin; Semen P. Khomets; Vlada A. Efremenko; Andrei V. Konstantinov; Natalya M. Verbova (CoRR, 2025)

Survival Analysis as Imprecise Classification with Trainable Kernels. - Andrei V. Konstantinov; Vlada A. Efremenko; Lev V. Utkin (CoRR, 2025)