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cs.LG2021★ 1 cited
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness
Glenn Dawson, Robi Polikar
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as class-conditional transition matrices. More recent work on instance-dependent n…
cs.LG2021
Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models
Muhammad Umer, Robi Polikar
Continual (or "incremental") learning approaches are employed when additional knowledge or tasks need to be learned from subsequent batches or from streaming data. However these ap…
cs.LG2021
OpinionRank: Extracting Ground Truth Labels from Unreliable Expert Opinions with Graph-Based Spectral Ranking
Glenn Dawson, Robi Polikar
As larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information…