Research map / Learning theory and algorithms / Prediction and decision losses
Probabilistic Invariant Learning with Randomized Linear Classifiers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RSetC instead of Deep Sets with hidden-layer size 5 | Set classification · Sorting task | accuracy | Helped |
| Use RSetC instead of Deep Sets with hidden-layer size 5 | Set classification · Sign task | accuracy | Helped |
| Test RSetC instead of Deep Sets on sets twice the training size | Set classification · Sorting task, out-of-distribution set sizes | accuracy | Helped |
| Test RSetC instead of Deep Sets on sets twice the training size | Set classification · Sign task, out-of-distribution set sizes | accuracy | Helped |
| Use RGraphC instead of GIN with hidden-layer size 2 | Graph classification · Connectivity task | accuracy | Helped |
Closest papers by meaning
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- Regularizing Towards Permutation Invariance In Recurrent Models (NeurIPS 2020 · similarity 0.85)
- Optimal Binary Classification Beyond Accuracy (NeurIPS 2022 · similarity 0.85)
- Estimating Learnability in the Sublinear Data Regime (NeurIPS 2018 · similarity 0.85)
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