Temporally-Consistent Survival Analysis

Abstract

We study survival analysis in the dynamic setting: We seek to model the time to an event of interest given sequences of states. Taking inspiration from temporal-difference learning, a central idea in reinforcement learning, we develop algorithms that estimate a discrete-time survival model by exploiting a temporal-consistency condition. Intuitively, this condition captures the fact that the survival distribution at consecutive states should be similar, accounting for the delay between states. Our method can be combined with any parametric survival model and naturally accommodates right-censored observations. We demonstrate empirically that it achieves better sample-efficiency and predictive performance compared to approaches that directly regress the observed survival outcome.

Related

April 2025 | 2024 IEEE Spoken Language Technology Workshop (SLT)

Classification Of Spontaneous And Scripted Speech For Multilingual Audio

Shahar Elisha, Andrew McDowell, Mariano Beguerisse-Díaz, Emmanouil Benetos

October 2024 | CIKM

PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters

A. Ghazimatin, E. Garmash, G. Penha, K. Sheets, M. Achenbach, O. Semerci, R. Galvez, M. Tannenberg, S. Mantravadi, D. Narayanan, O. Kalaydzhyan, D. Cole, B. Carterette, A. Clifton, P. N. Bennett, C. Hauff, M. Lalmas-Roelleke

October 2024 | Journal of Online Trust & Safety

Algorithmic Impact Assessments at Scale: Practitioners’ Challenges and Needs

Amar Ashar, Karim Ginena, Maria Cipollone, Renata Barreto, Henriette Cramer