
Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting
Restless and collapsing bandits are commonly used to model constrained r...
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Fair Clustering Under a Bounded Cost
Clustering is a fundamental unsupervised learning problem where a datase...
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Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes
Matching is one of the most fundamental and broadly applicable problems ...
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Fair Disaster Containment via GraphCut Problems
Graph cut problems form a fundamental problem type in combinatorial opti...
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A New Notion of Individually Fair Clustering: αEquitable kCenter
Clustering is a fundamental problem in unsupervised machine learning, an...
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Fairness, SemiSupervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Metric clustering is fundamental in areas ranging from Combinatorial Opt...
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Improved Approximation Algorithms for StochasticMatching Problems
We consider the Stochastic Matching problem, which is motivated by appli...
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Approximation Algorithms for RadiusBased, TwoStage Stochastic Clustering Problems with Budget Constraints
The main focus of this paper is radiusbased clustering problems in the ...
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A Pairwise Fair and Communitypreserving Approach to kCenter Clustering
Clustering is a foundational problem in machine learning with numerous a...
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Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During HighDemand Hours
Rideshare platforms, when assigning requests to drivers, tend to maximiz...
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Mix and Match: Markov Chains Mixing Times for Matching in Rideshare
Rideshare platforms such as Uber and Lyft dynamically dispatch drivers t...
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Vertexweighted Online Stochastic Matching with Patience Constraints
Online Bipartite Matching is a classic problem introduced by Karp, Vazir...
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Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity
In bipartite matching problems, vertices on one side of a bipartite grap...
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Approximation algorithms for stochastic clustering
We consider stochastic settings for clustering, and develop provablygoo...
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Attenuate Locally, Win Globally: An Attenuationbased Framework for Online Stochastic Matching with Timeouts
Online matching problems have garnered significant attention in recent y...
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Approximation algorithms for stochastic and riskaverse optimization
We present improved approximation algorithms in stochastic optimization....
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Allocation Problems in RideSharing Platforms: Online Matching with Offline Reusable Resources
Bipartite matching markets pair agents on one side of a market with agen...
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Algorithms to Approximate ColumnSparse Packing Problems
Columnsparse packing problems arise in several contexts in both determi...
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Aravind Srinivasan
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