Search Results for author: Aravind Srinivasan

Found 14 papers, 4 papers with code

Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks

no code implementations16 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.

Epidemiology

Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response

1 code implementation9 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.

Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual

no code implementations16 Jan 2022 Seyed A. Esmaeili, Sharmila Duppala, Vedant Nanda, Aravind Srinivasan, John P. Dickerson

In the most general form, the platform consists of three entities: two sides to be matched and a platform operator that decides the matching.

Fairness

Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting

no code implementations14 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.

Fairness

Fair Clustering Under a Bounded Cost

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.

Fairness

A New Notion of Individually Fair Clustering: $α$-Equitable $k$-Center

1 code implementation9 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.

Fairness

Fair Disaster Containment via Graph-Cut Problems

no code implementations9 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.

Combinatorial Optimization Fairness

Approximating Two-Stage Stochastic Supplier Problems

no code implementations7 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

Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

1 code implementation18 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.

Fairness

Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts

no code implementations22 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.

Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources

no code implementations22 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.

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