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Found 14 papers, 5 papers with code
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
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.
Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting
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.
Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts
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
Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources.
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare
Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests.
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Clustering is a foundational problem in machine learning with numerous applications.
Stochastic Optimization and Learning for Two-Stage Supplier Problems
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
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research.
Fair Disaster Containment via Graph-Cut Problems
Graph cut problems are fundamental in Combinatorial Optimization, and are a central object of study in both theory and practice.
A New Notion of Individually Fair Clustering: $α$-Equitable $k$-Center
Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications.
Fair Clustering Under a Bounded Cost
We derive fundamental lower bounds on the approximation of the utilitarian and egalitarian objectives and introduce algorithms with provable guarantees for them.
Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual
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.
Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response
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.
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model.
