9 papers
Explaining Rankings with Hidden Group Bonuses
Alvin Hong Yao Yan, Suraj Shetiya, Sujoy Bhore +2
Determining a linear utility function that correlates with observed candidate rankings is a foundational problem with applications in domains such as admissions, hiring, and recomm…
Fairness in Aggregation: Optimal Top- and Improved Full Ranking
Diptarka Chakraborty, Arya Mazumdar, Barna Saha +1
Ensuring fairness in algorithmic ranking systems is a critical challenge with significant societal implications for hiring, recommendations, web search, and data management. Standa…
Improved Rank Aggregation under Fairness Constraint
Diptarka Chakraborty, Himika Das, Sanjana Dey +1
Aggregating multiple input rankings into a consensus ranking is essential in various fields such as social choice theory, hiring, college admissions, web search, and databases. A m…
A Generic Framework for Fair Consensus Clustering in Streams
Diptarka Chakraborty, Kushagra Chatterjee, Debarati Das +1
Consensus clustering seeks to combine multiple clusterings of the same dataset, potentially derived by considering various non-sensitive attributes by different agents in a multi-a…
Maximizing Diversity in (near-)Median String Selection
Diptarka Chakraborty, Rudrayan Kundu, Nidhi Purohit +1
Given a set of strings over a specified alphabet, identifying a median or consensus string that minimizes the total distance to all input strings is a fundamental data aggregation…
Clustering with Label Consistency
Diptarka Chakraborty, Hendrik Fichtenberger, Bernhard Haeupler +3
Designing efficient, effective, and consistent metric clustering algorithms is a significant challenge attracting growing attention. Traditional approaches focus on the stability o…