:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kirpichenko, Stanislav R., Utkin, Lev V., Konstantinov, Andrei V., Verbova, Natalya M.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2502.05950
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)

Generating Survival Interpretable Trajectories and Data
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)

Dual feature-based and example-based explanation methods
by: Konstantinov, Andrei 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)

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

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)

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

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

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

Leakage and Interpretability in Concept-Based Models
by: Parisini, Enrico, et al.
Published: (2025)

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

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

Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning
by: Sun, Ao, et al.
Published: (2024)

Learning Discrete Concepts in Latent Hierarchical Models
by: Kong, Lingjing, et al.
Published: (2024)

Learning to Receive Help: Intervention-Aware Concept Embedding Models
by: Zarlenga, Mateo Espinosa, et al.
Published: (2023)

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

A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems
by: Wang, Yuan-Heng, et al.
Published: (2023)

LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning
by: Ye, Zhuorui, et al.
Published: (2024)

Learning Concept Bottleneck Models from Mechanistic Explanations
by: De Santis, Antonio, et al.
Published: (2026)

Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional and Transformer-Based Deep Learning Models
by: Apostol, Andrei, et al.
Published: (2025)

Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models
by: De Santis, Francesco, et al.
Published: (2025)

Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
by: Canizales, Ronaldo, et al.
Published: (2026)

Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach
by: Liu, Mingxuan, et al.
Published: (2025)

Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
by: Jain, Anchit, et al.
Published: (2026)

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

Distilling Symbolic Priors for Concept Learning into Neural Networks
by: Marinescu, Ioana, et al.
Published: (2024)

Constructive Symbolic Reinforcement Learning via Intuitionistic Logic and Goal-Chaining Inference
by: Patrascu, Andrei T.
Published: (2025)

SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
by: Tiofack, Franki Nguimatsia, et al.
Published: (2026)

Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models
by: Debot, David, et al.
Published: (2025)

Learning to Intervene on Concept Bottlenecks
by: Steinmann, David, et al.
Published: (2023)

SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks
by: Mastroleo, Marina, et al.
Published: (2026)

Interpretable Concept-Based Memory Reasoning
by: Debot, David, et al.
Published: (2024)

Compositional Learning of Visually-Grounded Concepts Using Reinforcement
by: Lin, Zijun, et al.
Published: (2023)

Representation Learning of Auxiliary Concepts for Improved Student Modeling and Exercise Recommendation
by: Badran, Yahya, et al.
Published: (2025)

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
by: Dominici, Gabriele, et al.
Published: (2024)

TRESTLE: A Model of Concept Formation in Structured Domains
by: MacLellan, Christopher J., et al.
Published: (2024)

Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
by: Bredis, George, et al.
Published: (2025)