Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
Proceedings of the IEEE conference on computer vision and pattern recognition , pages=
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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.
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.
citing papers explorer
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Response Time Enhances Alignment with Heterogeneous Preferences
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
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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.