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September 10, 2025Oberwolfach ReportsOpen Access

Overparametrization, Regularization, Identifiability and Uncertainty in Machine Learning

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Workshop explores uncertainty and identifiability issues in machine learning models, indicating diverse theoretical approaches.

Overparametrization and regularization methods impact the identifiability of machine learning models, highlighting key challenges.
Researchers discussed the effects of finite data on model behavior, revealing diverse algorithmic features that emerge.
The workshop utilized theoretical perspectives from distinct communities to address ill-posed operations prevalent in machine learning.
Insights gained from this workshop may enable more robust model design and understanding of uncertainty in machine learning contexts.

Cesa‐Bianchi et al. (2025) studied this question.

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September 10, 2025Oberwolfach ReportsOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Workshop explores uncertainty and identifiability issues in machine learning models, indicating diverse theoretical approaches.

Overparametrization and regularization methods impact the identifiability of machine learning models, highlighting key challenges.
Researchers discussed the effects of finite data on model behavior, revealing diverse algorithmic features that emerge.
The workshop utilized theoretical perspectives from distinct communities to address ill-posed operations prevalent in machine learning.
Insights gained from this workshop may enable more robust model design and understanding of uncertainty in machine learning contexts.

Cesa‐Bianchi et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark