Learning from Time Series for Health

ICLR 2026 Workshop | April 28/29

Contact: ts4h.chairs@gmail.com


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Call for Papers

The Learning from Time Series for Health (TS4H) workshop is returning to ICLR 2026, and invites submissions focused on the unique challenges and opportunities in modeling time series data to better understand and improve human health. Time series data is ubiquitous in modern healthcare, spanning diverse modalities such as wearables, sensors, medical waveforms, and electronic health records. This workshop brings together researchers working across methodological and applied areas to address questions unique to the intersection of time series analysis and health.

We invite you to submit your work by January 30, 2026 (Anywhere on Earth).

Topics of Interest

Submissions may explore but are not limited to:

Models & Methods

  • Novel Architectures: State-space models, diffusion models, etc.
  • Foundation Models: Pre-training, scaling, and alignment.
  • Deep Learning: Supervised, self-supervised, and unsupervised methods.
  • Probabilistic Modeling: Uncertainty quantification and Bayesian methods.
  • Sequential Decision-Making: Reinforcement learning and optimal control.

Data-Specific Challenges

  • Multimodal Learning: Fusing time series with images, text, or genomics, LLMs for time series data.
  • Irregular & Missing Data: Handling sparse or irregularly-sampled series.
  • Complex Signals: Modeling high-dimensional or multi-resolution data.
  • Causal Inference: Inferring cause-and-effect from observational data.
  • Representation Learning & Adaptation: Pre-training, adaptation strategies that transfer across patients, devices, test-time adaptation.

Applications & Trustworthy AI

  • Clinical Applications: Forecasting, risk stratification, digital biomarkers.
  • Trust & Reliability: Explainability, fairness, robustness, and privacy.
  • Deployment & Implementation: Real-world case studies and MLOps, federated evaluation, online learning during deployment.
  • New Resources: Public datasets, benchmarks, and software.

Submission Instructions

We invite short papers of up to 4 pages, excluding references and appendices. All submissions should follow the official ICLR'26 paper formatting guidelines (paper checklist is NOT needed) and be fully anonymized for a double-blind review process. Submitted work should be original and unpublished, though submissions on preprint servers like arXiv are permitted. Authors will be asked to confirm that their submissions accord with the ICLR Code of Conduct

All accepted papers must be presented in person at the workshop. This is a non-archival venue and there will be no formal proceedings.

OpenReview Submission

Important Dates

Submission Deadline: January 30, 2025 (AOE)

Workshop Date: April 28 or 29, 2025 (Co-located with ICLR'26 in Rio de Janeiro, Brazil)

Frequently Asked Questions (FAQ)

Q1. What does “non-archival venue” mean?
A non-archival venue is one where accepted papers are not formally published in proceedings. This means the work will not appear in a permanent, citable archive.

Q2. Does this mean accepted papers can be submitted elsewhere (e.g., to a journal or another conference)?
Yes! Since our workshop is non-archival, you are free to submit your work elsewhere. We do recommend checking the policies of your target journal or conference, as some may have restrictions on prior presentations — even at non-archival events.

Q3. Can I submit a paper that has been withdrawn from the main track of a conference?
Absolutely. You are welcome to submit such work to our workshop.

Q4. Can I submit the same work to your workshop and another venue (e.g., other ICLR workshop) at the same time?
Yes, concurrent submissions are allowed. Please ensure that your other target venue’s rules permit this.

Q5. Can I present work at the workshop that has already been accepted elsewhere?
Yes, provided that the other venue’s policies allow for prior or concurrent presentation at a non-archival workshop. Always confirm with the other venue first.