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Found 2 papers, 1 papers with code
Data-driven Policy Learning for a Continuous Treatment
This paper studies policy learning under the condition of unconfoundedness with a continuous treatment variable.
Compressing Features for Learning with Noisy Labels
This decomposition provides three insights: (i) it shows that over-fitting is indeed an issue for learning with noisy labels; (ii) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; (iii) it gives explanations on the performance boost brought by incorporating compression regularization into Co-teaching.
Ranked #10 on
Image Classification
on Clothing1M
(using extra training data)
