6 citations · 18 across the 33 of their papers we have counts for
36 papers
Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Data
Lev V. Utkin, Stanislav K. Kogan, Andrei V. Konstantinov
This work presents a novel attention-based framework for estimating the Individual Probability of Treatment Benefit (IPTB) in survival analysis contexts. The proposed model, called…
Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variation
Lev V. Utkin, Andrei V. Konstantinov, Stanislav K. Kogan +2
Estimating the probability that a treatment outperforms a control for an individual patient, called the Individual Probability of Treatment Benefit (IPTB), offers a clinically intu…
SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
Stanislav R. Kirpichenko, Andrei V. Konstantinov, Lev V. Utkin
Survival analysis aims to estimate a time-to-event distribution from data with censored observations. Many existing methods either impose structural assumptions on the hazard funct…
Towards a Relationship-Aware Transformer for Tabular Data
Andrei V. Konstantinov, Valerii A. Zuev, Lev V. Utkin
Deep learning models for tabular data typically do not allow for imposing a graph of external dependencies between samples, which can be useful for accounting for relatedness in ta…
Survival Analysis as Imprecise Classification with Trainable Kernels
Andrei V. Konstantinov, Vlada A. Efremenko, Lev V. Utkin
Survival analysis is a fundamental tool for modeling time-to-event data in healthcare, engineering, and finance, where censored observations pose significant challenges. While trad…
Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions
Lev V. Utkin, Semen P. Khomets, Vlada A. Efremenko +2
Survival analysis predicts the time until an event of interest, such as failure or death, but faces challenges due to censored data, where some events remain unobserved. Ensemble-b…
A novel gradient-based method for decision trees optimizing arbitrary differential loss functions
Andrei V. Konstantinov, Lev V. Utkin
There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable los…
Survival Concept-Based Learning Models
Stanislav R. Kirpichenko, Lev V. Utkin, Andrei V. Konstantinov +1
Concept-based learning enhances prediction accuracy and interpretability by leveraging high-level, human-understandable concepts. However, existing CBL frameworks do not address su…
SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms
Lev V. Utkin, Semen P. Khomets, Vlada A. Efremenko +1
Many ensemble-based models have been proposed to solve machine learning problems in the survival analysis framework, including random survival forests, the gradient boosting machin…
FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules
Lev V. Utkin, Andrei V. Konstantinov, Stanislav R. Kirpichenko
A method for solving concept-based learning (CBL) problem is proposed. The main idea behind the method is to divide each concept-annotated image into patches, to transform the patc…
Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning
Andrei V. Konstantinov, Lev V. Utkin
A problem of incorporating the expert rules into machine learning models for extending the concept-based learning is formulated in the paper. It is proposed how to combine logical…
Generating Survival Interpretable Trajectories and Data
Andrei V. Konstantinov, Stanislav R. Kirpichenko, Lev V. Utkin
A new model for generating survival trajectories and data based on applying an autoencoder of a specific structure is proposed. It solves three tasks. First, it provides prediction…
Dual feature-based and example-based explanation methods
Andrei V. Konstantinov, Boris V. Kozlov, Stanislav R. Kirpichenko +1
A new approach to the local and global explanation is proposed. It is based on selecting a convex hull constructed for the finite number of points around an explained instance. The…
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…
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…