Survival Analysis Paper List

A curated reading list of papers and resources in survival analysis, with an emphasis on machine learning, deep learning, calibration, evaluation, and applied time-to-event modeling.

Some papers may belong to multiple categories. I have organized each entry by what I view as its most significant contribution.

Since June 2026, I have also used LLM agents as research assistants to help discover, triage, and maintain survival-analysis papers while keeping the final curation human-reviewed.

Suggestions are welcome—please open an issue or pull request if you would like to add a paper or resource.

Table of Contents

Topic	Topic
Tutorials/Surveys	Perspective & Opinion
ML/DL Survival Models	Foundation Models
Objective Functions	Time-varying Covariates Models
Explainable Survival Models	Competing Risks & Multi-Event Models
Generalized Survival Analysis Methods	Evaluation Metrics
Causal Inference	Fairness
Out-Of-Distribution	Dependent Censoring
Synthetic Data Generation	Temporal Point Processes
Applications

---

Tutorials and Surveys

Title	Publisher	Date	Code	Notes
Machine Learning for Survival Analysis: A Survey	ACM Computing Surveys	2019.02		Slides
Calibration: the Achilles heel of predictive analytics	BMC Medicine	2019.12
A tutorial on calibration measurements and calibration models for clinical prediction models	JAMIA	2020.02	R
Survival analysis—time-to-event data and censoring	Nature Methods	2022.04
Survey: Strategies for Loss-Based Discrete-Time Hazard and Survival Function Estimation	ICTC	2022.10
Regression modeling of time-to-event data with censoring	Nature Methods	2022.11
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures	Bioinformatics	2022.09
Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review	Lancet Digital Health	2024.02
Deep learning for survival analysis: a review	Artificial Intelligence Review	2024.02
Review of Language Models for Survival Analysis	AAAI Spring Symposium on Clinical FMs	2024.05	PyTorch
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes	Foundation and Trends in Machine Learning	2024.12	PyTorch
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis	Computers in Biology and Medicine	2025.11	PyTorch

---

Perspective and Opinion

Title	Publisher	Date
Why Test for Proportional Hazards	JAMA	2020.03
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models	ArXiv	2025.06

---

ML and DL for Survival Analysis

Keyword	Title	Publisher	Date	Code	Notes
GBMCOX	The State of Boosting	Computing Science and Statistics	1999	R
RSF	Random Survival Forests	The Annals of Applied Statistics	2008	R
MTLR	Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors	NeurIPS	2011	R	Poster
DSA	Deep Survival Analysis	ML4HC	2016
N-MTLR	Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework	ArXiv	2018.01	Python
DeepSurv	DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network	BMC Medical Research Methodology	2018.02	Python
DATE & DRAFT	Adversarial Time-to-Event Modeling	ICML	2018.07	TensorFlow
CoxTime / CoxCC	Time-to-Event Prediction with Neural Networks and Cox Regression	JMLR	2019.08	PyTorch	PyCox 1-3
PCHazard / LogisticHazard	Continuous and Discrete-Time Survival Prediction with Neural Networks	Lifetime Data Analysis	2021.10	PyTorch	PyCox 2-3
SurvivalQuilts	Temporal Quilting for Survival Analysis	AISTATS	2020.04	Python
SCA	Survival Cluster Analysis	ACM CHIL	2020.04	TensorFlow
VAECox	Improved survival analysis by learning shared genomic information from pan-cancer data	Bioinformatics	2020.07	PyTorch
DCM	Deep Cox Mixtures for Survival Regression	MLHC	2021.08	TensorFlow
DHBN	Using Discrete Hazard Bayesian Networks to Identify which Features are Relevant at each Time in a Survival Prediction Model	AAAI Spring Symposium (SP-ACA)	2021.03	R
TDSA	Transformer-Based Deep Survival Analysis	AAAI Spring Symposium (SP-ACA)	2021.03
DeepQuantreg	Deep learning for quantile regression under right censoring: DeepQuantreg	Computational Statistics and Data Analysis	2021.07	TensorFlow
IWSG	Inverse-Weighted Survival Games	NeurIPS	2021.12	PyTorch
DeepEH	Deep Extended Hazard Models for Survival Analysis	NeurIPS	2021.12
VaDeSC	A Deep Variational Approach to Clustering Survival Data	ICLR	2022.03	TensorFlow
ODE-Cox	Survival Analysis via Ordinary Differential Equations	JASA	2022
DeSurv	Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis	AISTATS	2022.05	PyTorch
Survival MDN	Survival Mixture Density Networks	MLHC	2022.08	PyTorch
SODEN	SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks	JMLR	2022	PyTorch
DCS	Deep Learning-Based Discrete Calibrated Survival Prediction	ICDH	2022.08	PyTorch
CQRNN	Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis	NeurIPS	2022.12	PyTorch	Poster
MSSDA	Multi-Source Survival Domain Adaptation	AAAI	2023.06
DH-MNN	Metaparametric Neural Networks for Survival Analysis	TNNLS	2023.08
NSOTree	Neural Survival Oblique Tree	ArXiv	2023.09	Python
NNCDE	Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data	JMLR	2023.12	PyTorch
NFM	Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions	NeurIPS	2023.12	PyTorch
Diffsurv	Differentiable sorting for censored time-to-event data	NeurIPS	2023.12	PyTorch
OSST	Optimal Sparse Survival Trees	AISTATS	2024.05	Python
Survival Kernets	Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee	JMLR	2024.02	PyTorch
deepAFT	deepAFT: A nonlinear accelerated failure time model with artificial neural network	Statistics in Medicine	2024.06	R
SurvReLU	Inherently Interpretable Survival Analysis via Deep ReLU Networks	CIKM	2024.10	Code
FastSurvival	FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models	NeurIPS	2024.12
ConSurv	Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning	NeurIPS	2024.12	PyTorch
L2Boost-CUT / L2Boost-IMP	Boosting Methods for Interval-censored Data with Regression and Classification	ICLR	2025.02	R
CDVI	Censor Dependent Variational Inference	ICML	2025.07	PyTorch
ALD	Learning Survival Distributions with the Asymmetric Laplace Distribution	ICML	2025.07	PyTorch
Spectral	Spectral Survival Analysis	KDD	2025.08
NeuralSurv	NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification	NeurIPS	2025.12	JAX
ICALD	Learning Survival Distributions with Individually Calibrated Asymmetric Laplace Distribution	ICLR	2026.01	Python
SALaD	Explicitly Modeling Censoring Produces Superior Survival Predictors	ICML	2026.07	PyTorch

---

Foundation Models and Pretraining

Keyword	Title	Publisher	Date	Code	Notes
MOTOR	MOTOR: A Time-to-Event Foundation Model For Structured Medical Records	ICLR	2024.01	PyTorch
TTE-Pretrain	Time-to-Event Pretraining for 3D Medical Imaging	ICLR	2025.02
TabSurv-FM	Tabular Foundation Model for Breast Cancer Prognosis using Gene Expression Data	medRxiv	2025.10	Python
TFM-Surv	Tabular Foundation Models Can Do Survival Analysis	ArXiv	2026.01
SIC	Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks	ArXiv	2026.03
ICareFM	A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at Scale	medRxiv	2026.04	Code
SurvivEHR	SurvivEHR: a competing risks, time-to-event foundation model for multiple long-term conditions from primary care electronic health records	npj Digital Medicine	2026.05
SurvivalPFN	SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference	FMSD Workshop	2026.05	Python
TabSA-BJ	Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models	FMSD Workshop	2026.06
SurvPFN	SurvPFN: Towards Foundation Models for Survival Predictions	FMSD Workshop	2026.06

---

Objective Functions

Keyword	Title	Publisher	Date	Code	Notes
GBMCI	A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index	Computational and Mathematical Methods in Medicine	2013.09	R
Survival-CRPS	Countdown Regression: Sharp and Calibrated Survival Predictions	UAI	2019	PyTorch
	Bias in Cross-Entropy-Based Training of Deep Survival Networks	TPAMI	2020.03
SFM	Calibration and Uncertainty in Neural Time-to-Event Modeling	TNNLS	2020.09	TensorFlow
X-CAL	X-CAL: Explicit Calibration for Survival Analysis	NeurIPS	2020.12	PyTorch	Poster
Discrete-RPS	Estimating Calibrated Individualized Survival Curves with Deep Learning	AAAI	2021.02	PyTorch
KL-Calibration	Simpler Calibration for Survival Analysis	OpenReview	2021.10
SuMo-net	Survival regression with proper scoring rules and monotonic neural networks	AISTATS	2022.03	PyTorch
DQS	Proper Scoring Rules for Survival Analysis	ICML	2023.07	PyTorch	Poster
	A Strictly Proper Scoring Rule and a Calibration Metric for Interval-Censored Data Analysis	ICML	2026.07	PyTorch

---

Time-varying Covariates Models

Keyword	Title	Publisher	Date	Code	Notes
SPH	Survival Prediction by an Integrated Learning Criterion on Intermittently Varying Healthcare Data	AAAI	2016.02
Time-varying Cox	Time-varying covariates and coefficients in Cox regression models	Annals of Translational Medicine	2018.01
DRSA	Deep Recurrent Survival Analysis	AAAI	2019.02	TensorFlow
TSNN	Time-Dependent Survival Neural Network for Remaining Useful Life Prediction	PAKDD	2019.03
Dynamic-DeepHit	Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data	TBME	2019.04	TensorFlow
TSNN & RSNN	Survival neural networks for time-to-event prediction in longitudinal study	Knowledge and Information Systems	2020.05		Extend Paper
DRSM	Deep Parametric Time-to-Event Regression with Time-Varying Covariates	AAAI Spring Symposium (SP-ACA)	2021.03	PyTorch
SubLign	Clustering Interval-Censored Time-Series for Disease Phenotyping	AAAI	2022.02
TCSA	Temporally-Consistent Survival Analysis	NeurIPS	2022.11	Python	Poster
SurvPP	Survival Permanental Processes for Survival Analysis with Time-Varying Covariates	NeurIPS	2023.12	Python
CoxSig	Dynamic Survival Analysis with Controlled Latent States	ICML	2024.07	PyTorch
DSPS	Support vector machine for dynamic survival prediction with time-dependent covariates	Ann. Appl. Stat.	2024.09	R

---

Explainable Survival Models

Keyword	Title	Publisher	Date	Code	Notes
SPIE	Simultaneous Prediction Intervals for Patient-Specific Survival Curves	IJCAI	2019	Python
SurvLIME	SurvLIME: A method for explaining machine learning survival models	Knowledge-Based Systems	2020.09	Python
AutoScore-Survival	AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data	Journal of Biomedical Informatics	2022.01	R
EXCEL	Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction	ArXiv	2022.09
BNN-ISD	Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction	IEEE TBME	2023.07	PyTorch
GradSHAP(t)	Gradient-based Explanations for Deep Learning Survival Models	ICML	2025.07	R
DyS	Interpretable Prediction and Feature Selection for Survival Analysis	KDD	2025.08
SurvFD / SurvSHAP-IQ	Functional Decomposition and Shapley Interactions for Interpreting Survival Models	ICML	2026.07	Python
CoxSubgroup	Subgroup Discovery with the Cox Model	ICML	2026.07	Python

---

Competing Risks and Multi-Event Models

Keyword	Title	Publisher	Date	Code	Notes
	On pseudo-values for regression analysis in competing risks models	Lifetime Data Analysis	2009.06
	The Survival Filter: Joint Survival Analysis with a Latent Time Series	UAI	2015
DMGP	Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks	NeurIPS	2017.12
DeepHit	DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks	AAAI	2018.02	TensorFlow
DSM	Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks	IEEE JBHI	2021.01	PyTorch
HSA	A Hierarchical Approach to Multi-Event Survival Analysis	AAAI	2021.05	PyTorch
SurvTRACE	SurvTRACE: Transformers for Survival Analysis with Competing Events	ACM BCB	2022.08	PyTorch
Deep-CSA	Deep-CSA: Deep Contrastive Learning for Dynamic Survival Analysis with Competing Risks	IEEE JBHI	2022.04
DeepPseudo	DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis	AAAI	2021.05
SurvivalBoost	Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks	AISTATS	2025.05
CompRiskCal	On the calibration of survival models with competing risks	AISTATS	2026.02	Python
MENSA	MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation	ML4H	2026	PyTorch
DiSKD	Discrete Survival Knowledge Distillation for Competing Risks Analysis	ICML	2026.07	Python

---

Generalized Survival Analysis Methods

	Title	Publisher	Date	Code	Notes
Pseudo-observations	Pseudo-observations in survival analysis	Statistical Methods in Medical Research	2010
	A doubly robust censoring unbiased transformation	The International Journal of Biostatistics	2007.03
	Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting	Journal of Biomedical Informatics	2016.03	R
	A General Machine Learning Framework for Survival Analysis	ECML-PKDD	2020.09	R
Parametrics Pseudo-observations	Regression models using parametric pseudo-observations	Statistics in Medicine	2020.06
	A Kernel Log-Rank Test of Independence for Right-Censored Data	JASA	2021.09	Python
CSA	Conformalized survival analysis	JRSS: Series B	2023.01	R
anchor-vis	A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information	CHIL	2023.07	Python
Adaptive-CSA	Conformalized survival analysis with adaptive cut-offs	Biometrika	2023.12	R
CSD	Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration	ICML	2024.05	Python	Poster
CiPOT	Toward Conditional Distribution Calibration in Survival Prediction	NeurIPS	2024.12	Python	Poster
Fused	Conformalized Survival Analysis for General Right-Censored Data	ICLR	2025.02
DR-CSA	Doubly Robust Conformalized Survival Analysis with Right-Censored Data	ICML	2025.07	Code
SurvUnc	SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis	KDD	2025.08
KSP	KSP: Kolmogorov-Smirnov metric-based Post-Hoc Calibration for Survival Analysis	NeurIPS	2025.12	Python
LLM-Surv-LPB	Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs	AISTATS	2026.02	Python
CompositeDistill	Component-Wise Composite Likelihood Distillation for Censored Time-to-Event Data	ICML	2026.07	Python
COLSA	Online Learning and Inference for Cox Proportional Hazards Model Using Renewable Sieve Estimation	ICML	2026.07	R

---

Evaluation Metrics

Keyword	Title	Publisher	Date	Code	Notes
Brier Score Decomposition	The Comparison and Evaluation of Forecasters	The Statistician	1983
CRPS Decomposition	Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems	Weather and Forecasting	2000.03
IPCW Brier Score	Assessment and Comparison of Prognostic Classification Schemes for Survival Data	Statistics in Medicine	1999.09
Administrative Brier Score	The Brier Score under Administrative Censoring: Problems and a Solution	JMLR	2023	PyTorch	PyCox 3-3
Graphical-Cal	Graphical calibration curves and the integrated calibration index (ICI) for survival models	Statistics in Medicine	2019.11	Python
D-Calibration	Effective Ways to Build and Evaluate Individual Survival Distributions	JMLR	2020.06	R
KSD	Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data	ICML	2020.07
	Scoring rules in survival analysis	ArXiv	2022.12
MAE-PO	An Effective Meaningful Way to Evaluate Survival Models	ICML	2023.07	PyTorch	Poster
SurvivalEVAL	SurvivalEVAL: A Comprehensive Open-Source Python Package for Evaluating Individual Survival Distributions	AAAI Symposium Series	2024.01	Python
KM-ARL / KM-ADD	Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis	ICML	2026.07	Python
	When Can We Trust Survival Model Evaluation ?	ICML	2026.07	Python

---

Causal Inference

Keyword	Title	Publisher	Date	Code	Notes
	Causal inference in survival analysis using pseudo-observations	Statistics in Medicine	2017.03
CausalTree	Causal Inference for Survival Analysis	ArXiv	2018.03	R
CSA	Enabling Counterfactual Survival Analysis with Balanced Representations	ACM CHIL	2021.03	Python
SurvITE	SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data	NeurIPS	2021.10	TensorFlow
CMHE	Counterfactual Phenotyping with Censored Time-to-Events	KDD	2022.02	PyTorch
DNMC	Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data	AISTATS	2022.03	TensorFlow
compCATE	Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data	AISTATS	2023.02	Python
	Causal Identification for Complex Functional Longitudinal Studies	ICLR	2025.02
PCI2S	Regression-based proximal causal inference for right-censored time-to-event data	Epidemiology	2025.06	R
	Doubly Protected Estimation for Survival Outcomes Utilizing External Controls for Randomized Clinical Trials	ICML	2025.05	R
FairHTE	Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data	AISTATS	2025.05	R
MISTR	Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees	ICML	2025.07	Code
DoseSurv	DoseSurv: Predicting Personalized Survival Outcomes under Continuous-Valued Treatments	NeurIPS	2025.12
OSL	Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data	NeurIPS	2025.12
MP-RL	Beyond Average Value Function in Precision Medicine: Maximum Probability-Driven Reinforcement Learning for Survival Analysis	NeurIPS	2025.12	Python
SurvHTE-Bench	SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis	ICLR	2026.01	Python
FuseSurv	Privacy-Protected Causal Survival Analysis Under Distribution Shift	ICLR	2026.01	R
CSCP	Conformalized Survival Counterfactuals Prediction for General Right-Censored Data	ICLR	2026.01

---

Fairness

Keyword	Title	Publisher	Date	Code	Notes
FSRF	Longitudinal Fairness with Censorship	AAAI	2022.03
FISA	Fair and Interpretable Models for Survival Analysis	KDD	2022.08		Video
IFS	Censored Fairness through Awareness	AAAI	2023.03
	Fairness-Aware Processing Techniques in Survival Analysis: Promoting Equitable Predictions	ECML-PKDD	2023.09
DRO-Cox	Fairness in Survival Analysis with Distributionally Robust Optimization	JMLR	2024.08	PyTorch
FairFSA	Fair Federated Survival Analysis	AAAI	2025.04

---

Out-Of-Distribution

Keyword	Title	Publisher	Date	Code	Notes
	Evaluating Domain Generalization for Survival Analysis in Clinical Studies	CHIL	2022.08
Stable-Cox	Stable Cox regression for survival analysis under distribution shifts	Nature Machine Intelligence	2024.12	PyTorch

---

Dependent Censoring and Competing Risks

Keyword	Title	Publisher	Date	Code	Notes
	Copula Based Cox Proportional Hazards Models for Dependent Censoring	JASA	2023.03	R
CopulaDeepSurvival	Copula-Based Deep Survival Models for Dependent Censoring	UAI	2023.06	PyTorch
DCSurvival	Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees	AAAI	2024.03	PyTorch
PSA	Proximal survival analysis to handle dependent right censoring	JRSS: Series B	2024.05
HACSurv	HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks	AISTATS	2025.05	PyTorch
SC-Net	Survival Analysis via Density Estimation	ICML	2025.02	PyTorch
CopulaMetrics	Practical Evaluation of Copula-based Survival Metrics: Beyond the Independent Censoring Assumption	ArXiv	2025.02

---

Synthetic Data Generation

Keyword	Title	Publisher	Date	Code	Notes
SurvivalGAN	SurvivalGAN: Generating Time-to-Event Data for Survival Analysis	AISTATS	2023.02	PyTorch
	SYNDSURV: A simple framework for survival analysis with data distributed across multiple institutions	Computers in Biology and Medicine	2024.04	PyTorch
	Conditioning on Time is All You Need for Synthetic Survival Data Generation	ArXiv	2024.05	PyTorch
SurvDiff	SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis	ICML	2026.07	Python

Temporal Point Processes

Title	Publisher	Date	Code	Notes
Lecture Notes: Temporal Point Processes and the Conditional Intensity Function	ArXiv	2018.06
Temporal Point Processes	Course Material	2019.01
Recent Advance in Temporal Point Process: from Machine Learning Perspective		2019
Wavelet Reconstruction Networks for Marked Point Processes	AAAI Spring Symposium (SP-ACA)	2021.03	Python
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations	ICLR	2024.01
Approximating Drift-Diffusion Models for User Decisions under Nudging and External Information	ICML	2026.07

---

Applied Survival Analysis

Title	Publisher	Date	Code	Notes
Empirical comparisons between Kaplan-Meier and Nelson-Aalen survival function estimators	Journal of Statistical Computation and Simulation	2002
Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data	Patterns	2020.08	Python
Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database	Lancet Digital Health	2021.03	Python
Learning accurate personalized survival models for predicting hospital discharge and mortality of COVID-19 patients	Scientific Reports	2022.03	R
Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis	Neurobiology of Aging	2023.01
Time-to-event modeling for hospital length of stay prediction for COVID-19 patients	Machine Learning with Applications	2022.09
Metabolomic profiles predict individual multidisease outcomes	Nature Medicine	2022.09	PyTorch
Personalized breast cancer onset prediction from lifestyle and health history information	PLOS One	2022.12
SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes	ICML	2023.06	TensorFlow	Poster
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model	ICML	2023.06	R	Poster
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records	CIKM	2023.10	Python
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction	ICLR	2024.01	PyTorch
Multimodal Prototyping for cancer survival prediction	ICML	2024.05	PyTorch
Predicting Long-Term Allograft Survival in Liver Transplant Recipients	ML4HC	2024.08	PyTorch
Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images	NeurIPS	2024.12	PyTorch	Poster
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology	ICLR	2025.02	PyTorch
VAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes	Computer Methods and Programs in Biomedicine	2025.03	PyTorch
From Representation Space to Prognostic Insights: Whole Slide Image Generation with Hierarchical Diffusion Model for Survival Prediction	AAAI	2025.04
CA-MLIF: Cross-Attention and Multimodal Low-Rank Interaction Fusion Framework for Tumor Prognostic Prediction	AAAI	2025.04
Cancer Survival Analysis via Zero-shot Tumor Microenvironment Segmentation on Low-resolution Whole Slide Pathology Images	NeurIPS	2025.12
A Bayesian Approach to Contextual Dynamic Pricing using the Proportional Hazards Model with Discrete Price Data	NeurIPS	2025.12
Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks	ICLR	2026.01
ConSurv: Multimodal Continual Learning for Survival Analysis	AAAI	2026.03	PyTorch
Cancer Survival Prediction by Cyclic Generation and Multi-grained Alignment	AAAI	2026.03	Code
Mind the Gap: Predicting, Explaining and Reducing Time-to-First-Comment (Reply Gap) in Online Mental-Health Communities	AAAI	2026.03
DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole Slide Image Survival Prediction	ICML	2026.07	PyTorch
FunCQNet: A Functional Censored Quantile Neural Network for Predicting Long-Term Post-Transplant Kidney Survival	ICML	2026.07	Python
SVL: Goal-Conditioned Reinforcement Learning as Survival Learning	ICML	2026.07	Python