Online Survival Analysis: A Bandit Approach under Cox PH Model
2026-04-23T07:23:59Z•7135ddeb0bf64d89f5a07f35fe0aaa4d8021f0ec9a3eaef5a57adfc53f1d5c2b
Cox-PHERM-RERFisher-RaoGibbs-measuresLangevinRenyi-DPRicci-curvatureRiemannian-manifoldsWassersteinarxivbanditscausal-inference','symbolic-computation','Gröbner-bases','identfcensoringdecentralized-learningdelayed-feedbackdiscretizationfederated-learninggeometric-temperinggradient-flowheat-diffusionmachine-learningonline-learningprivacystatistical-learningsurvival-analysis
What happened
ArXiv stat/ML feed (2026-04-23) containing multiple new submissions across online learning, privacy, federated/decentralized learning, causal inference, optimization, and statistical theory. Key papers: 1) "Online Survival Analysis: A Bandit Approach under Cox PH Model" — introduces online bandit adaptations of Cox proportional-hazards survival analysis addressing staggered entry, delayed feedback and censoring with sublinear regret and experiments on SEER cancer data. 2) "Properties and limitations of geometric tempering for gradient flow dynamics" — analyzes geometric tempering for Wasserst‑
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 7135ddeb0bf64d89f5a07f35fe0aaa4d8021f0ec9a3eaef5a57adfc53f1d5c2b
- Enrichment time
- 2026-04-23T07:23:59Z
- AI-assisted enrichment
- Yes
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