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Stanislav Kirpichenko, Lev Utkin, Andrei Konstantinov and Vladimir Muliukha
A method for estimating the conditional average treatment effect under the condition of censored time-to-event data, called BENK (the Beran Estimator with Neural Kernels), is proposed. The main idea behind the method is to apply the Beran estimator for e...
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Andrei Konstantinov, Lev Utkin and Vladimir Muliukha
A new random forest-based model for solving the Multiple Instance Learning problem under small tabular data, called the Soft Tree Ensemble Multiple Instance Learning, is proposed. A new type of soft decision trees is considered, which is similar to the w...
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Andrei Konstantinov, Stanislav Kirpichenko and Lev Utkin
A new method for estimating the conditional average treatment effect is proposed in this paper. It is called TNW-CATE (the Trainable Nadaraya?Watson regression for CATE) and based on the assumption that the number of controls is rather large and the numb...
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Lev Utkin, Andrey Ageev, Andrei Konstantinov and Vladimir Muliukha
A new modification of the isolation forest called the attention-based isolation forest (ABIForest) is proposed for solving the anomaly detection problem. It incorporates an attention mechanism in the form of Nadaraya?Watson regression into the isolation ...
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Andrei Konstantinov, Lev Utkin and Vladimir Muliukha
This paper provides new models of the attention-based random forests called LARF (leaf attention-based random forest). The first idea behind the models is to introduce a two-level attention, where one of the levels is the ?leaf? attention, and the attent...
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Lev Utkin and Andrei Konstantinov
The ensemble-based modifications of the well-known SHapley Additive exPlanations (SHAP) method for the local explanation of a black-box model are proposed. The modifications aim to simplify the SHAP which is computationally expensive when there is a larg...
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