Agnostic Language Identification and Generation: Relaxing Realizability Assumptions
A new paper on arXiv (2601.23258) introduces an agnostic framework for language identification and generation, removing the standard realizability assumption that input data must be supported on a known language. The authors propose novel objectives and derive nearly tight statistical rates for both tasks under this relaxed setup.
Key facts
- The paper is titled 'Agnostic Language Identification and Generation'.
- It is categorized under Computer Science > Machine Learning.
- The work relaxes the realizability assumption common in prior research.
- No restrictions are imposed on the distribution of input data.
- The authors propose objectives for language identification and generation.
- They obtain novel characterizations and nearly tight rates.
- The paper is available on arXiv with ID 2601.23258.
Entities
Institutions
- arXiv