14 citations · 19 across the 8 of their papers we have counts for
8 papers
Bayesian Strategic Classification
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl +2
In strategic classification, agents modify their features, at a cost, to ideally obtain a positive classification from the learner's classifier. The typical response of the learner…
On the Vulnerability of Fairness Constrained Learning to Malicious Noise
Avrim Blum, Princewill Okoroafor, Aadirupa Saha +1
We consider the vulnerability of fairness-constrained learning to small amounts of malicious noise in the training data. Konstantinov and Lampert (2021) initiated the study of this…
Agnostic Multi-Robust Learning Using ERM
Saba Ahmadi, Avrim Blum, Omar Montasser +1
A fundamental problem in robust learning is asymmetry: a learner needs to correctly classify every one of exponentially-many perturbations that an adversary might make to a test-ti…
Sequential Strategic Screening
Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl +2
We initiate the study of strategic behavior in screening processes with multiple classifiers. We focus on two contrasting settings: a conjunctive setting in which an individual mus…
Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline
Avrim Blum, Kevin Stangl, Ali Vakilian
Consider an actor making selection decisions using a series of classifiers, which we term a sequential screening process. The early stages filter out some applicants, and in the fi…
Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?
Avrim Blum, Kevin Stangl
Multiple fairness constraints have been proposed in the literature, motivated by a range of concerns about how demographic groups might be treated unfairly by machine learning clas…
Upper and Lower Bounds on the Speed of a One Dimensional Excited Random Walk
Erin Bossen, Brian Kidd, Owen Levin +3
Excited random walks (ERWs) are a self-interacting non-Markovian random walk in which the future behavior of the walk is influenced by the number of times the walk has previously v…
A Practical Study of Longitudinal Reference Based Compressed Sensing for MRI
Samuel Birns, Bohyun Kim, Stephanie Ku +2
Compressed sensing (CS) is a new signal acquisition paradigm that enables the reconstruction of signals and images from a low number of samples. A particularly exciting application…