Methods › General › Regularization › LVR

Low Variance Regularization

LVR

4 papers tagged archive 2025-07-28

Introduced by Shahed Masoudian et al. in Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Method introduces a novel unlabeled debiasing technique which works on classification task to reduce the bias of the transformer based language models on downstream classification task. In their method authors use the classes as metric for regularization and punish the network if the embedding produced by the model are far from each other. by doing so the authors claim to be able to reduce the domain shift caused by any unwanted attribute information hence results in fair embedding.

PaperSource

Papers archive 2025-07-28

4 shown of 4, 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.

Tasks archive 2025-07-28

4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Attribute1
Language Modeling1
Language Modelling1
Sentence1

Usage over time archive 2025-07-28

Papers per year tagged with LVR: 2024 to 2025, peak 2 2 0 2024: 2 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

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

Regularization

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