no code implementations • 16 Feb 2022 • Amy Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model.
1 code implementation • 9 Feb 2022 • George Li, Ann Li, Madhav Marathe, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2.
no code implementations • 16 Jan 2022 • Seyed A. Esmaeili, Sharmila Duppala, Davidson Cheng, Vedant Nanda, Aravind Srinivasan, John P. Dickerson
Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator's profit.
no code implementations • NeurIPS 2021 • Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan, John P. Dickerson
We derive fundamental lower bounds on the approximation of the utilitarian and egalitarian objectives and introduce algorithms with provable guarantees for them.
1 code implementation • 14 Jun 2021 • Christine Herlihy, Aviva Prins, Aravind Srinivasan, John P. Dickerson
Restless and collapsing bandits are often used to model budget-constrained resource allocation in settings where arms have action-dependent transition probabilities, such as the allocation of health interventions among patients.
no code implementations • 9 Jun 2021 • Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
Graph cut problems are fundamental in Combinatorial Optimization, and are a central object of study in both theory and practice.
1 code implementation • 9 Jun 2021 • Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas
Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications.
1 code implementation • 2 Mar 2021 • Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research.
no code implementations • 7 Aug 2020 • Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the form of a budget constraint.
Data Structures and Algorithms
no code implementations • ICML 2020 • Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas
Clustering is a foundational problem in machine learning with numerous applications.
1 code implementation • 18 Dec 2019 • Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman, John P. Dickerson, Aravind Srinivasan
Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e. g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver.
no code implementations • 30 Nov 2019 • Michael J. Curry, John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Yuhao Wan, Pan Xu
Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests.
no code implementations • 22 Apr 2018 • Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu
On the upper bound side, we show that this framework, combined with a black-box adapted from Bansal et al., (Algorithmica, 2012), yields an online algorithm which nearly doubles the ratio to 0. 46.
no code implementations • 22 Nov 2017 • John P. Dickerson, Karthik A. Sankararaman, Aravind Srinivasan, Pan Xu
Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources.