Robustness in Sequence Modeling
Форма участия: Очная
Срок подачи заявок: 02.10.2022
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?
Where appropriate, we encourage authors to add discussions of any ethical considerations relevant to the presented work.
We invite extended abstract submissions that are 3-4 pages long (not including references). All accepted papers will be presented in person as posters and lightning talks. There are no formal proceedings generated from this workshop. Authors are encouraged to make their work publicly available through our online listing of presented work. The reviewing process will be double-blind. Please submit anonymized versions of your paper that include no identifying information about any author identities or affiliations. Submitted papers must be new work that has not yet been published.