{"total":58,"rows":[{"id":"2305.14961","title":"Deep Learning for Survival Analysis: A Review","created":"2023-05-24","cited_by_count":75,"area":null},{"id":"2101.06536","title":"Deep Cox Mixtures for Survival Regression","created":"2021-01-16","cited_by_count":44,"area":null},{"id":"2103.14755","title":"Survival Regression with Proper Scoring Rules and Monotonic Neural Networks","created":"2021-03-26","cited_by_count":42,"area":null},{"id":"2110.00855","title":"SurvTRACE: Transformers for Survival Analysis with Competing Events","created":"2021-10-02","cited_by_count":41,"area":null},{"id":"2101.05346","title":"X-CAL: Explicit Calibration for Survival Analysis","created":"2021-01-13","cited_by_count":30,"area":null},{"id":"2111.08386","title":"Towards Generating Real-World Time Series Data","created":"2021-11-16","cited_by_count":26,"area":null},{"id":"2106.05763","title":"A Deep Variational Approach to Clustering Survival Data","created":"2021-06-10","cited_by_count":25,"area":null},{"id":"2110.14001","title":"SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data","created":"2021-10-26","cited_by_count":25,"area":null},{"id":"2204.07276","title":"auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data","created":"2022-04-15","cited_by_count":17,"area":null},{"id":"2302.12749","title":"SurvivalGAN: Generating Time-to-Event Data for Survival Analysis","created":"2023-02-24","cited_by_count":17,"area":null},{"id":"2104.08903","title":"SurvNAM: The machine learning survival model explanation","created":"2021-04-18","cited_by_count":16,"area":null},{"id":"2202.11089","title":"Counterfactual Phenotyping with Censored Time-to-Events","created":"2022-02-22","cited_by_count":13,"area":null},{"id":"2204.09633","title":"SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction","created":"2022-04-20","cited_by_count":13,"area":null},{"id":"2202.07423","title":"DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis","created":"2022-02-12","cited_by_count":12,"area":null},{"id":"2206.10477","title":"Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee","created":"2022-06-21","cited_by_count":9,"area":null},{"id":"2208.10759","title":"Survival Mixture Density Networks","created":"2022-08-23","cited_by_count":9,"area":null},{"id":"2111.08175","title":"Inverse-Weighted Survival Games","created":"2021-11-16","cited_by_count":8,"area":null},{"id":"2203.00144","title":"The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models","created":"2022-02-28","cited_by_count":8,"area":null},{"id":"2603.29475","title":"Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks","created":"2026-03-31","cited_by_count":6,"area":null},{"id":"2308.13104","title":"Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records","created":"2023-08-24","cited_by_count":5,"area":null},{"id":"2107.12825","title":"Individual Survival Curves with Conditional Normalizing Flows","created":"2021-07-27","cited_by_count":4,"area":null},{"id":"2207.03099","title":"A State Transition Model for Mobile Notifications via Survival Analysis","created":"2022-07-07","cited_by_count":4,"area":null},{"id":"2312.14651","title":"SAVAE: Leveraging the variational Bayes autoencoder for survival analysis","created":"2023-12-22","cited_by_count":4,"area":null},{"id":"2105.01009","title":"Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality","created":"2021-05-03","cited_by_count":3,"area":null},{"id":"2210.11366","title":"Deep conditional transformation models for survival analysis","created":"2022-10-20","cited_by_count":3,"area":null},{"id":"2301.11826","title":"Deep Clustering Survival Machines with Interpretable Expert Distributions","created":"2023-01-27","cited_by_count":3,"area":null},{"id":"2306.05120","title":"Explainable Predictive Maintenance","created":"2023-06-08","cited_by_count":3,"area":null},{"id":"2402.12331","title":"Generating Survival Interpretable Trajectories and Data","created":"2024-02-19","cited_by_count":3,"area":null},{"id":"2305.06862","title":"A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information","created":"2023-05-11","cited_by_count":2,"area":null},{"id":"2405.17333","title":"Generating Accurate Synthetic Survival Data by Conditioning on Outcomes","created":"2024-05-27","cited_by_count":2,"area":null},{"id":"2407.19371","title":"Deep State-Space Generative Model For Correlated Time-to-Event Predictions","created":"2024-07-28","cited_by_count":2,"area":null},{"id":"2505.14803","title":"SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2511.11935","title":"SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis","created":"2025-11-14","cited_by_count":2,"area":null},{"id":"2606.19361","title":"Computational Identifiability","created":"2026-06-08","cited_by_count":2,"area":null},{"id":"2102.02295","title":"Variational Bayes survival analysis for unemployment modelling","created":"2021-02-03","cited_by_count":1,"area":null},{"id":"2107.08784","title":"Boost-R: Gradient Boosted Trees for Recurrence Data","created":"2021-07-03","cited_by_count":1,"area":null},{"id":"2211.10793","title":"BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect","created":"2022-11-19","cited_by_count":1,"area":null},{"id":"2305.14655","title":"Learning Survival Distribution with Implicit Survival Function","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2309.13825","title":"NSOTree: Neural Survival Oblique Tree","created":"2023-09-25","cited_by_count":1,"area":null},{"id":"2311.01660","title":"Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling","created":"2023-11-03","cited_by_count":1,"area":null},{"id":"2312.13454","title":"MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records","created":"2023-12-20","cited_by_count":1,"area":null},{"id":"2406.14009","title":"Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2502.09591","title":"Censor Dependent Variational Inference","created":"2025-02-13","cited_by_count":1,"area":null},{"id":"2507.14160","title":"FinSurvival: A Suite of Large Scale Survival Modeling Tasks from Finance","created":"2025-07-07","cited_by_count":1,"area":null},{"id":"2508.09328","title":"Dynamic Survival Prediction using Longitudinal Images based on Transformer","created":"2025-08-12","cited_by_count":1,"area":null},{"id":"2101.01835","title":"Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: a machine learning approach","created":"2021-01-06","cited_by_count":0,"area":null},{"id":"2110.06610","title":"Metaparametric Neural Networks for Survival Analysis","created":"2021-10-13","cited_by_count":0,"area":null},{"id":"2212.12028","title":"Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm","created":"2022-12-22","cited_by_count":0,"area":null},{"id":"2310.15853","title":"Improving Event Time Prediction by Learning to Partition the Event Time Space","created":"2023-10-24","cited_by_count":0,"area":null},{"id":"2312.05854","title":"Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores","created":"2023-12-10","cited_by_count":0,"area":null},{"id":"2401.02708","title":"TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival Analysis","created":"2024-01-05","cited_by_count":0,"area":null},{"id":"2503.09097","title":"Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2507.07804","title":"Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2508.13189","title":"Preference Models assume Proportional Hazards of Utilities","created":"2025-08-15","cited_by_count":0,"area":null},{"id":"2509.06575","title":"Robust and Adaptive Spectral Method for Representation Multi-Task Learning with Contamination","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2509.20152","title":"C$^2$MIL: Synchronizing Semantic and Topological Causalities in Multiple Instance Learning for Robust and Interpretable Survival Analysis","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2605.16208","title":"A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2610.02868","title":"Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}