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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 2781–2790 of 9051 papers

TitleStatusHype
Diversity-Oriented Data Augmentation with Large Language Models—0
Diversity Over Quantity: A Lesson From Few Shot Relation Classification—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Diversity Preference-Aware Link Recommendation for Online Social Networks—0
Diversity Preferences, Affirmative Action and Choice Rules—0
Diversity-Preserving K-Armed Bandits, Revisited—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
Diversity-Promoting Bayesian Learning of Latent Variable Models—0
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
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