Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks
When data are right-censored, i.e. some outcomes are missing due to a limited period of observation, survival analysis can compute the "time to event". Multiple classes of outcomes lead to a classification variant: predicting the most likely event, known as competing risks, which has been less studied. To build a loss that estimates outcome probabilities for such settings, we introduce a strictly proper censoring-adjusted separable scoring rule that can be optimized on a subpart of the data because the evaluation is made independently of observations. It enables stochastic optimization for competing risks which we use to train gradient boosting trees. Compared to 11 state-of-the-art models, this model, MultiIncidence, performs best in estimating the probability of outcomes in survival and competing risks. It can predict at any time horizon and is much faster than existing alternatives.
Code (0)
등록된 구현이 없습니다.
Tasks
scoring ruleStochastic OptimizationSurvival AnalysisSimilar Papers 제목 키워드 기반
How Proper Scoring Rules Shape LLM Forecasting
This paper evaluates how reward function choice shapes the performance and behavior of LLM forecasters. We compare five proper scoring rules as training objectives for binary forecasts of resolved real-world events. Alth…
Aligned Textual Scoring Rules
Scoring rules elicit probabilistic predictions from a strategic agent by scoring the prediction against a ground truth state. A scoring rule is proper if, from the agent's perspective, reporting the true belief maximizes…
scoring ruleSurvival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks
When dealing with right-censored data, where some outcomes are missing due to a limited observation period, survival analysis -- known as time-to-event analysis -- focuses on predicting the time until an event of interes…
scoring ruleStochastic OptimizationSurvival AnalysisProper scoring rules for estimation and forecast evaluation
Proper scoring rules have been a subject of growing interest in recent years, not only as tools for evaluation of probabilistic forecasts but also as methods for estimating probability distributions. In this article, we …
Likelihood-ratio calibration using prior-weighted proper scoring rules
Prior-weighted logistic regression has become a standard tool for calibration in speaker recognition. Logistic regression is the optimization of the expected value of the logarithmic scoring rule. We generalize this via …
regressionscoring ruleSpeaker Recognition