This workshop aims to facilitate progress towards improving the distributional robustness of models trained on sequential data by bringing together researchers to tackle a wide variety of research questions. We encourage submissions on topics including but not limited to:
- How well do existing robustness methods work on sequential data, and when or why do they succeed or fail?
- Can we directly predict or otherwise characterize the performance of models on sequential data under distribution shifts?
- How can we leverage the sequential nature of data to develop novel and distributionally robust methods?
- What kinds of guarantees can we derive on predictive performance under distribution shifts, and how can we formalize these shifts?
- Submission deadline: October 2nd, 2022 (Anywhere on Earth)
- Decision notification: October 20th, 2022
- Workshop event: December 2nd, 2022, In-person in New Orleans, LA, USA.
Submission URL: https://openreview.net/group?id=NeurIPS.cc/2022/Workshop/RobustSeq
We invite extended abstract submissions that are 4 pages long (5 pages for camera-ready), plus unlimited pages for references and appendices. Please format your submission using these LaTeX style files.
Please submit anonymized versions of your paper that include no identifying information about any author identities or affiliations. There are no formal proceedings generated from this workshop. Accepted papers will be made public on OpenReview. The reviewing process will be double-blind. Submitted papers must be new work that has not yet been published.