10 citations · 15 across the 7 of their papers we have counts for
7 papers
Block-Coordinate Methods and Restarting for Solving Extensive-Form Games
Darshan Chakrabarti, Jelena Diakonikolas, Christian Kroer
Coordinate descent methods are popular in machine learning and optimization for their simple sparse updates and excellent practical performance. In the context of large-scale seque…
Stackelberg POMDP: Learning to Lead via Reinforcement Learning
Gianluca Brero, Alon Eden, Darshan Chakrabarti +6
Many real-world domains--including e-commerce platform design, security planning, and multi-agent coordination--feature leader-follower problems where one decision-maker commits to…
Optimal Local Bayesian Differential Privacy over Markov Chains
Darshan Chakrabarti, Jie Gao, Aditya Saraf +2
In the literature of data privacy, differential privacy is the most popular model. An algorithm is differentially private if its outputs with and without any individual's data are…
Implications of Distance over Redistricting Maps: Central and Outlier Maps
Seyed A. Esmaeili, Darshan Chakrabarti, Hayley Grape +1
In representative democracy, a redistricting map is chosen to partition an electorate into districts which each elects a representative. A valid redistricting map must satisfy a co…
A New Notion of Individually Fair Clustering: -Equitable -Center
Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili +2
Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications. In this…
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Brian Brubach, Darshan Chakrabarti, John P. Dickerson +2
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research. However, in a variety of situations…
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach, Darshan Chakrabarti, John P. Dickerson +3
Clustering is a foundational problem in machine learning with numerous applications. As machine learning increases in ubiquity as a backend for automated systems, concerns about fa…