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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

Showing 29312940 of 9051 papers

TitleStatusHype
Local Diversity of Condorcet DomainsCode0
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup IdentificationCode0
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction0
SuperCLUE-Math6: Graded Multi-Step Math Reasoning Benchmark for LLMs in ChineseCode2
What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders0
Enhancing Recommendation Diversity by Re-ranking with Large Language Models0
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Large-scale Reinforcement Learning for Diffusion Models0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects0
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