Quantifying Learning Guarantees for Convex but Inconsistent Surrogates

Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin

Nov-20-2025, 15:46:39 GMT–Neural Information Processing Systems

We extend the recent framework of Osokin et al.

artificial intelligence, machine learning, optimization problem, (19 more...)

Neural Information Processing Systems

Nov-20-2025, 15:46:39 GMT

Conferences PDF

Country:
- North America > Canada
  - Quebec > Montreal (0.04)
- Europe > Russia
  - Central Federal District > Moscow Oblast > Moscow (0.04)
- Asia
  - Russia (0.14)
  - Middle East > Jordan (0.04)

Genre:
- Research Report (0.46)

Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning > Statistical Learning (0.69)
  - Representation & Reasoning > Optimization (0.46)

Duplicate Docs Excel Report

Title
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates

Similar Docs Excel Report more

Title	Similarity	Source
None found