1 citations · 3 across the 8 of their papers we have counts for
13 papers
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
Stanislav R. Kirpichenko, Lev V. Utkin, Andrei V. Konstantinov
A method for estimating the conditional average treatment effect under condition of censored time-to-event data called BENK (the Beran Estimator with Neural Kernels) is proposed. T…
LARF: Two-level Attention-based Random Forests with a Mixture of Contamination Models
Andrei V. Konstantinov, Lev V. Utkin
New models of the attention-based random forests called LARF (Leaf Attention-based Random Forest) are proposed. The first idea behind the models is to introduce a two-level attenti…
Improved Anomaly Detection by Using the Attention-Based Isolation Forest
Lev V. Utkin, Andrey Y. Ageev, Andrei V. Konstantinov
A new modification of Isolation Forest called Attention-Based Isolation Forest (ABIForest) for solving the anomaly detection problem is proposed. It incorporates the attention mech…
Attention-based Random Forest and Contamination Model
Lev V. Utkin, Andrei V. Konstantinov
A new approach called ABRF (the attention-based random forest) and its modifications for applying the attention mechanism to the random forest (RF) for regression and classificatio…
Attention-like feature explanation for tabular data
Andrei V. Konstantinov, Lev V. Utkin
A new method for local and global explanation of the machine learning black-box model predictions by tabular data is proposed. It is implemented as a system called AFEX (Attention-…
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
One of the most popular methods of the machine learning prediction explanation is the SHapley Additive exPlanations method (SHAP). An imprecise SHAP as a modification of the origin…