activity
20122022
most citedRepairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions

26 citations · 60 across the 17 of their papers we have counts for

collaborators

28 papers

cs.CV2022

Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks

Marguerite B. Basta, Sarfaraz Hussein, Hsiang Hsu +1

Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection…

cs.IT2021

Measuring Information from Moments

Wael Alghamdi, Flavio P. Calmon

We investigate the problem of representing information measures in terms of the moments of the underlying random variables. First, we derive polynomial approximations of the condit…

cs.IT2021

-Approximate Coded Matrix Multiplication is Nearly Twice as Efficient as Exact Multiplication

Haewon Jeong, Ateet Devulapalli, Viveck R. Cadambe +1

We study coded distributed matrix multiplication from an approximate recovery viewpoint. We consider a system of computation nodes where each node stores of each multipli…

cs.IT2021

Polynomial Approximations of Conditional Expectations in Scalar Gaussian Channels

Wael Alghamdi, Flavio P. Calmon

We consider a channel where is a random variable satisfying and is an independent standard normal random variable. We show that the minimum…

cs.IR2021

Privacy-Preserving Near Neighbor Search via Sparse Coding with Ambiguation

Behrooz Razeghi, Sohrab Ferdowsi, Dimche Kostadinov +2

In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding. The core of the framework relies on sparse coding…

cs.IT20209 cited

Bottleneck Problems: Information and Estimation-Theoretic View

Shahab Asoodeh, Flavio Calmon

Information bottleneck (IB) and privacy funnel (PF) are two closely related optimization problems which have found applications in machine learning, design of privacy algorithms, c…