:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Konstantinov, Andrei V., Kirpichenko, Stanislav R., Utkin, Lev V.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2402.12331
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TabSurv: Adapting Modern Tabular Neural Networks to Survival Analysis
by: Kirpichenko, Stanislav, et al.
Published: (2026)

SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
by: Kirpichenko, Stanislav R., et al.
Published: (2026)

Survival Concept-Based Learning Models
by: Kirpichenko, Stanislav R., et al.
Published: (2025)

Dual feature-based and example-based explanation methods
by: Konstantinov, Andrei V., et al.
Published: (2024)

FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules
by: Utkin, Lev V., et al.
Published: (2024)

Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning
by: Konstantinov, Andrei V., et al.
Published: (2024)

Survival Analysis as Imprecise Classification with Trainable Kernels
by: Konstantinov, Andrei V., et al.
Published: (2025)

A novel gradient-based method for decision trees optimizing arbitrary differential loss functions
by: Konstantinov, Andrei V., et al.
Published: (2025)

Towards a Relationship-Aware Transformer for Tabular Data
by: Konstantinov, Andrei V., et al.
Published: (2025)

SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms
by: Utkin, Lev V., et al.
Published: (2024)

Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions
by: Utkin, Lev V., et al.
Published: (2025)

Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images
by: Dumaev, Rinat I., et al.
Published: (2024)

Automated and Interpretable Survival Analysis from Multimodal Data
by: Malafaia, Mafalda, et al.
Published: (2025)

Deep Clustering Survival Machines with Interpretable Expert Distributions
by: Hou, Bojian, et al.
Published: (2023)

Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
by: Kowal, Matthew, et al.
Published: (2026)

EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis
by: Tripathi, Aakash, et al.
Published: (2025)

The Approximate Fisher Influence Function: Faster Estimation of Data Influence in Statistical Models
by: Lev, Omri, et al.
Published: (2024)

SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis
by: Brockschmidt, Marie, et al.
Published: (2025)

(GG) MoE vs. MLP on Tabular Data
by: Chernov, Andrei
Published: (2025)

Neural Diffusion Processes for Physically Interpretable Survival Prediction
by: Cristofoletto, Alessio, et al.
Published: (2025)

Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery
by: Zheng, Yuhan, et al.
Published: (2024)

An Alternative Trajectory for Generative AI
by: Belova, Margarita, et al.
Published: (2026)

Handling and Interpreting Missing Modalities in Patient Clinical Trajectories via Autoregressive Sequence Modeling
by: Wang, Andrew, et al.
Published: (2026)

Enhancing Interpretability in Generative AI Through Search-Based Data Influence Analysis
by: Aivalis, Theodoros, et al.
Published: (2025)

Is Data Valuation Learnable and Interpretable?
by: Wu, Ou, et al.
Published: (2024)

Data Interpreter: An LLM Agent For Data Science
by: Hong, Sirui, et al.
Published: (2024)

PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
by: Schlender, Thalea, et al.
Published: (2025)

Kinematic Detection of Anomalies in Human Trajectory Data
by: Kennedy, Lance, et al.
Published: (2024)

Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron
by: Wang, Yuan-Heng, et al.
Published: (2024)

Gradient-Based Program Synthesis with Neurally Interpreted Languages
by: Macfarlane, Matthew V., et al.
Published: (2026)

Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics
by: Wang, Yuan-Heng, et al.
Published: (2024)

Budget-constrained Active Learning to Effectively De-censor Survival Data
by: Parsaee, Ali, et al.
Published: (2025)

Topological Deep Learning with State-Space Models: A Mamba Approach for Simplicial Complexes
by: Montagna, Marco, et al.
Published: (2024)

Offline Reinforcement Learning with Generative Trajectory Policies
by: Feng, Xinsong, et al.
Published: (2025)

Revealing Interpretable Failure Modes of VLMs
by: Chaudhary, Isha, et al.
Published: (2026)

AIM: Attributing, Interpreting, Mitigating Data Unfairness
by: Liu, Zhining, et al.
Published: (2024)

Interpretable Graph Neural Networks for Tabular Data
by: Alkhatib, Amr, et al.
Published: (2023)

Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration
by: Qi, Shi-ang, et al.
Published: (2024)

Unlocking Biomedical Insights: Hierarchical Attention Networks for High-Dimensional Data Interpretation
by: Nair, Rekha R, et al.
Published: (2025)

Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments
by: Andres, Alain, et al.
Published: (2023)