Hugging Face Blog
Using Machine Learning to Aid Survivors and Race through Time
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arXiv AI
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Capture Timing-Attention of Events in Clinical Time Series
arXiv:2602. 10385v5 Announce Type: replace-cross Abstract: The contemporary paradigm of trajectory learning operates fundamentally at the level of group dynamics, systematically reducing individual-level complexity to fit group-level models, thus rendering effective patient subtyping difficult and individual-level modeling largely out of reach.
arXiv AI
Jun 26
To Use AI as Dice of Possibilities with Timing Computation
arXiv:2605. 01134v3 Announce Type: replace Abstract: The dominant noun-based modeling paradigm has fundamentally constrained AI development, precluding any adequate representation of the future as an open temporal dimension.
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.
arXiv AI
Jul 1
A time-series classification framework for individual-level absenteeism prediction under severe class imbalance
arXiv:2606. 31532v1 Announce Type: new Abstract: Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and delivery services, where proactive workforce planning depends on reliable individual-level absence prediction.
arXiv Machine Learning
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Tabular Foundation Models Can Do Survival Analysis
arXiv:2601. 22259v2 Announce Type: replace Abstract: While tabular foundation models have achieved remarkable success in classification and regression, adapting them to model time-to-event outcomes for survival analysis is non-trivial due to right-censoring, where data observations may end before the event of interest occurs.
arXiv AI
Sep 16
Continuous-Time Machine Learning: A Unified Mathematical Perspective
The paper surveys continuous‑time (CT) machine learning, a framework for modeling temporal dynamics as continuous processes, especially useful when data are sampled irregularly or over long horizons. It introduces a unified taxonomy that groups major CT methods by their underlying mathematical formulations and shows how different architectural choices—such as vector‑field parameterization, stochasticity, memory mechanisms, and discretization—relate these families. The survey compares training algorithms, optimization strategies, failure modes, computational complexity, and benchmarks, reviews supporting software ecosystems, and outlines open challenges and future research directions.