Comments (1)
Yup! Copy-pasting from #7,
That's right, each batch may contain many groups. Sorry for the confusion! This is what we were referring to in Section 5 of the paper with
Concretely, we interleave SGD on θ and exponentiated gradient ascent on q (Algorithm 1). In practice, we use minibatches and a momentum term for θ; see Appendix C.2 for details.)
We wrote the single-example case in Algorithm 1 to make the core idea clearer and avoid too much notation, but in practice we do batch updates.
Hmm, regarding that comment, I don't think it's justified. Our algorithm uses a generic data loader. We'll contact the authors of that paper. Thanks for letting us know!
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Related Issues (13)
- Could you provide the csv files? HOT 1
- ERM should also save best model based on worst-group accuracy HOT 1
- Codalab worksheet link is broken HOT 1
- Setting of ERM baseline HOT 1
- The places2 dataset in the provided link is under maintenance.
- not being able to reproduce your results on MNLI HOT 2
- generating MNLI glue files HOT 1
- not being able to reproduce your results on CUB and CelebA HOT 2
- Questions about the L2-penalties
- Why is reweight_groups flag set for DRO algorithm? Possible unfair comparison to ERM? HOT 1
- Where is the random group chosen? HOT 3
- Invalid link HOT 1
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