arXiv Machine Learning

Non-Crossing Deep Quantile Regression for Distributional Survival Prediction

arXiv:2608. 16864v1 Announce Type: cross Abstract: In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number.

arXiv Machine Learning

Aug 11

Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression

arXiv:2608. 08204v1 Announce Type: cross Abstract: This work proposes deep nonparametric Instrumental variable quantile regression (IVQR), a two-stage estimator that combines conditional diffusion modeling with a kernel-smoothed conditional moment formulation.

By Xingdong Feng, Xinhong Jiang, Yuling Jiao, Lican Kang, Junwei Liu

diffusion

arXiv Machine Learning

Jun 4

SurvPFN: Towards Foundation Models for Survival Predictions

arXiv:2606. 04564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched.

By Samuel B\"ohm (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany), Lennart Purucker (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Frank Hutter (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Pascal Schlosser (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, US, CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany)

arXiv Machine Learning

Jul 22

When Are Scoring Rules Proper? Bridging Theory and Practice in Survival Model Evaluation

arXiv:2212. 05260v4 Announce Type: replace-cross Abstract: Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated workflows such as AutoML.

By John Zobolas, Raphael Sonabend, Riccardo De Bin, Johannes Piller, Philipp Kopper, Lukas Burk, Andreas Bender

arXiv AI

Jun 3

Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models

arXiv:2606. 03689v1 Announce Type: cross Abstract: Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs.

By Mariana Vargas Vieyra

benchmarks

arXiv Machine Learning

Aug 6

A Multi-Cohort Validation of Censoring-Aware Conformal Lower Predictive Bounds for Pathology Survival Models

arXiv:2608. 04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times.

By Mingi Hong

arXiv Machine Learning

1d ago

Convolution Smoothed Quantile Regression for XGBoost

arXiv:2608. 15290v1 Announce Type: cross Abstract: The increasing availability of large and complex datasets across many scientific disciplines has led to widespread adoption of machine learning (ML) for prediction.

By Mandy Yao (University of Toronto), Meredith Franklin (University of Toronto)

benchmarks

arXiv AI

1d ago

CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction

arXiv:2608. 16594v1 Announce Type: new Abstract: Cancer survival prediction supports treatment planning, risk stratification, and follow-up management.

By Tianqi Xiang, Qixiang Zhang, Xinpeng Ding, Yi Li, Xiaomeng Li

llms multimodal benchmarks

arXiv Machine Learning

Jul 27

SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis

arXiv:2509. 22352v3 Announce Type: replace Abstract: Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death.

By Marie Brockschmidt, Maresa Schr\"oder, Stefan Feuerriegel

diffusion benchmarks

arXiv Machine Learning

Jul 14

Pitfalls of Administrative Censoring in Survival Models with Time-Indexed Inputs

arXiv:2607. 10466v1 Announce Type: new Abstract: Survival models can model time-to-event outcomes using partially observed data.

By Yanqi Xu, Hui Dai, Carlos Fernandez-Granda, Krzysztof J. Geras, Yiqiu Shen

safety

arXiv Machine Learning

Jun 5

Proper Scoring Rules for Right-Censored Survival Data

arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.

By Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke

arXiv Machine Learning

Jun 25

Efficient Analytic Uncertainty Quantification for Multi-Modal Regression

arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.

By Kun Jin, James Harrison, Jiawei Li, Sihan Liu, Jiayi Liu, Randolph Linderman, Yuening Li, Arnab Bhadury, Sourabh Prakash Bansod, Liang Liu, Jasper Snoek

multimodal benchmarks

arXiv Machine Learning

Aug 4

A reproducible and extensible framework for benchmarking competing risks survival models

arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.

By Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos

benchmarks safety

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