30 citations · 54 across the 27 of their papers we have counts for
7 papers · 1 filter
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…
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression
Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin
A new method for estimating the conditional average treatment effect is proposed in the paper. It is called TNW-CATE (the Trainable Nadaraya-Watson regression for CATE) and based o…
AGBoost: Attention-based Modification of Gradient Boosting Machine
Andrei Konstantinov, Lev Utkin, Stanislav Kirpichenko
A new attention-based model for the gradient boosting machine (GBM) called AGBoost (the attention-based gradient boosting) is proposed for solving regression problems. The main ide…
Attention and Self-Attention in Random Forests
Lev V. Utkin, Andrei V. Konstantinov
New models of random forests jointly using the attention and self-attention mechanisms are proposed for solving the regression problem. The models can be regarded as extensions of…