95 citations · 645 across the 72 of their papers we have counts for
7 papers · 1 filter
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman +2
Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that d…
Group Fairness in Bandit Arm Selection
Candice Schumann, Zhi Lang, Nicholas Mattei +1
We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting. In the CMAB setting, a sequential decision maker must, at…
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare
Michael J. Curry, John P. Dickerson, Karthik Abinav Sankararaman +3
Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests. We model the dispatching process in rideshare as a Markov chain that takes into ac…
Deep k-NN Defense against Clean-label Data Poisoning Attacks
Neehar Peri, Neal Gupta, W. Ronny Huang +5
Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples i…
An Algorithm for Multi-Attribute Diverse Matching
Saba Ahmadi, Faez Ahmed, John P. Dickerson +2
Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas su…
Making the Cut: A Bandit-based Approach to Tiered Interviewing
Candice Schumann, Zhi Lang, Jeffrey S. Foster +1
Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e…