Exploiting data symmetries boosts k-NN to select near-optimal low-noise subsets from noisy datasets, approaching Bayes-optimal performance in high dimensions, with learned representations aiding partial symmetry knowledge.
Advances in neural information processing systems , volume=
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RGBT combines GMM-derived instance reliability weights with a Bayes-label transition matrix to achieve consistent, low-variance estimation from noisy implicit feedback while using all samples.
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Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
Exploiting data symmetries boosts k-NN to select near-optimal low-noise subsets from noisy datasets, approaching Bayes-optimal performance in high dimensions, with learned representations aiding partial symmetry knowledge.
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Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework
RGBT combines GMM-derived instance reliability weights with a Bayes-label transition matrix to achieve consistent, low-variance estimation from noisy implicit feedback while using all samples.