22 citations · 42 across the 26 of their papers we have counts for
33 papers
Beyond Worst-Case Coreset Bounds for -Clustering via Determinantal Sampling
Diptarka Chakraborty, Satyaki Mukherjee, Gaurav Vallabhdas Revankar +1
Massive datasets in modern machine learning have made data reduction a central challenge, particularly for clustering tasks where memory and computational constraints demand compac…
Ulam Rank Aggregation Is Hard to Approximate for Four Rankings
Sk Ruhul Azgor, Diptarka Chakraborty, Le Van Cuong +3
We study the approximability of rank aggregation under the Ulam metric. In the \emph{Ulam median} problem, the goal is to find a ranking (permutation) minimizing the sum of its Ula…
Hardness of Approximation of Rank Aggregation on Ulam Metric
Sk Ruhul Azgor, Diptarka Chakraborty, Le Van Cuong +2
We study the approximability of rank aggregation under the Ulam metric. In the \emph{Ulam median} problem, the goal is to find a permutation minimizing the sum of its Ulam distance…
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…
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…