Methods › General › Robust Training › CW-ERM
Closed-loop Weighted Empirical Risk Minimization
CW-ERM
Introduced by Eesha Kumar et al. in CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
A closed-loop evaluation procedure is first used in a simulator to identify training data samples that are important for practical driving performance and then we these samples to help debias the policy network.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization 5 Oct 2022 · 1 repository · arXiv:2210.02174
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Autonomous Driving | 1 |
| Imitation Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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