6 citations · 9 across the 10 of their papers we have counts for
10 papers
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…
TabSurv: Adapting Modern Tabular Neural Networks to Survival Analysis
Stanislav Kirpichenko, Andrei Konstantinov, Lev Utkin
Survival analysis on tabular data is a well-studied problem. However, existing deep learning methods are often highly task-specific, which can limit the transfer of new approaches…
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…
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…
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…
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…
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…