Survival analysis is a branch of statistics focused on analyzing and modeling the time until an event of interest occurs, such as death, failure, or relapse. It is particularly useful in scenarios where data may be censored, meaning the exact event time is unknown for some individuals. In the abstract, survival analysis involves using the Cox model to predict hazard functions and evaluate survival times, enabling subgroup discovery and the identification of interpretable patient cohorts.

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NEC Labs America Attends ICML 2026 Seoul, South Korea July 6-11, 2026

NEC Laboratories America researchers are heading to Seoul this July for ICML 2026, the Forty-Third International Conference on Machine Learning. One of the most prestigious gatherings in the field, ICML draws academic and industry researchers from around the world to share work spanning machine learning, artificial intelligence, data science, and their many applications.

Subgroup Discovery with the Cox Model

We study the problem of subgroup discovery with Cox regression models and introduce a method for finding an interpretable subset of the data on which a Cox model is highly accurate. Our method relies on two technical innovations: the emph (Unknown sysvar: (expected prediction entropy)), a novel metric for evaluating survival models which predict a hazard function; and the emph (Unknown sysvar: (conditional rank distribution)), a statistical object which quantifies the deviation of an individual point to the distribution of survival times in an existing subgroup. Because of the interpretability of the discovered subgroups, in addition to improving the predictive accuracy of the model, they can also form meaningful, data-driven patient cohorts for further study in a clinical setting.

NEC Labs America Team Attends NeurIPS24 in Vancouver

NEC Labs America is proud to attend NeurIPS 2024 in Vancouver, Canada from December 10-15. Zachary Izzo will present Subgroup Discovery with the Cox Model, Shaobo Han will present VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks and Jonathan Warrell will present Discrete-Continuous Variational Optimization with Local Gradients.