58 citations · 96 across the 9 of their papers we have counts for
9 papers
Consistency as a Testable Property: Statistical Methods to Evaluate AI Agent Reliability
Harsh Raj, Niranjan Orkat, Suvrorup Mukherjee +3
This paper establishes a rigorous measurement science for AI agent reliability, providing a foundational framework for quantifying consistency under semantically preserving perturb…
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes
Cheryl Flynn, Aritra Guha, Subhabrata Majumdar +2
New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society. For example: the COVID-19 pandemic highlighted dispariti…
Detecting Bias in the Presence of Spatial Autocorrelation
Subhabrata Majumdar, Cheryl Flynn, Ritwik Mitra
In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or m…
Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity
Victor A. E. Farias, Felipe T. Brito, Cheryl Flynn +3
Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for re…
Towards Integrating Fairness Transparently in Industrial Applications
Emily Dodwell, Cheryl Flynn, Balachander Krishnamurthy +2
Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML lifecy…
Composing Differential Privacy and Secure Computation: A case study on scaling private record linkage
Xi He, Ashwin Machanavajjhala, Cheryl Flynn +1
Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one…
On the Sensitivity of the Lasso to the Number of Predictor Variables
Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff
The Lasso is a computationally efficient regression regularization procedure that can produce sparse estimators when the number of predictors (p) is large. Oracle inequalities prov…
Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models
Cheryl J. Flynn, Clifford M. Hurvich, Jeffrey S. Simonoff
It has been shown that AIC-type criteria are asymptotically efficient selectors of the tuning parameter in non-concave penalized regression methods under the assumption that the po…
Profile Likelihood Biclustering
Cheryl J. Flynn, Patrick O. Perry
Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. M…