Suresh Venkatasubramanian

is this you? claim profile

∙ 0 followers
THE UNIVERSITY OF UTAH

Professor at University of Utah

A Research Ecosystem for Secure Computing

Computing devices are vital to all areas of modern life and permeate eve...
01/04/2021 ∙ by Nadya Bliss, et al. ∙ 0 ∙ share

read it
Interdisciplinary Approaches to Understanding Artificial Intelligence's Impact on Society

Innovations in AI have focused primarily on the questions of "what" and ...
12/11/2020 ∙ by Suresh Venkatasubramanian, et al. ∙ 0 ∙ share

read it
Evolving Methods for Evaluating and Disseminating Computing Research

Social and technical trends have significantly changed methods for evalu...
07/02/2020 ∙ by Benjamin Zorn, et al. ∙ 0 ∙ share

read it
Fair clustering via equitable group representations

What does it mean for a clustering to be fair? One popular approach seek...
06/19/2020 ∙ by Mohsen Abbasi, et al. ∙ 21 ∙ share

read it
Problems with Shapley-value-based explanations as feature importance measures

Game-theoretic formulations of feature importance have become popular as...
02/25/2020 ∙ by I. Elizabeth Kumar, et al. ∙ 19 ∙ share

read it
Equalizing Recourse across Groups

The rise in machine learning-assisted decision-making has led to concern...
09/07/2019 ∙ by Vivek Gupta, et al. ∙ 28 ∙ share

read it
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions

Motivated by the need to audit complex and black box models, there has b...
06/20/2019 ∙ by Charles T. Marx, et al. ∙ 8 ∙ share

read it
Fairness in representation: quantifying stereotyping as a representational harm

While harms of allocation have been increasingly studied as part of the ...
01/28/2019 ∙ by Mohsen Abbasi, et al. ∙ 8 ∙ share

read it
Sublinear Algorithms for MAXCUT and Correlation Clustering

We study sublinear algorithms for two fundamental graph problems, MAXCUT...
02/20/2018 ∙ by Aditya Bhaskara, et al. ∙ 0 ∙ share

read it
A comparative study of fairness-enhancing interventions in machine learning

Computers are increasingly used to make decisions that have significant ...
02/13/2018 ∙ by Sorelle A. Friedler, et al. ∙ 0 ∙ share

read it
Fair Pipelines

This work facilitates ensuring fairness of machine learning in the real ...
07/03/2017 ∙ by Amanda Bower, et al. ∙ 0 ∙ share

read it
Runaway Feedback Loops in Predictive Policing

Predictive policing systems are increasingly used to determine how to al...
06/29/2017 ∙ by Danielle Ensign, et al. ∙ 0 ∙ share

read it
On the (im)possibility of fairness

What does it mean for an algorithm to be fair? Different papers use diff...
09/23/2016 ∙ by Sorelle A. Friedler, et al. ∙ 0 ∙ share

read it
A Unified View of Localized Kernel Learning

Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting...
03/04/2016 ∙ by John Moeller, et al. ∙ 0 ∙ share

read it
Auditing Black-box Models for Indirect Influence

Data-trained predictive models see widespread use, but for the most part...
02/23/2016 ∙ by Philip Adler, et al. ∙ 0 ∙ share

read it
A Group Theoretic Perspective on Unsupervised Deep Learning

Why does Deep Learning work? What representations does it capture? How d...
04/08/2015 ∙ by Arnab Paul, et al. ∙ 0 ∙ share

read it
Why does Deep Learning work? - A perspective from Group Theory

Why does Deep Learning work? What representations does it capture? How d...
12/20/2014 ∙ by Arnab Paul, et al. ∙ 0 ∙ share

read it
Certifying and removing disparate impact

What does it mean for an algorithm to be biased? In U.S. law, unintentio...
12/11/2014 ∙ by Michael Feldman, et al. ∙ 0 ∙ share

read it
Rethinking Abstractions for Big Data: Why, Where, How, and What

Big data refers to large and complex data sets that, under existing appr...
06/14/2013 ∙ by Mary Hall, et al. ∙ 0 ∙ share

read it
A Geometric Algorithm for Scalable Multiple Kernel Learning

We present a geometric formulation of the Multiple Kernel Learning (MKL)...
06/25/2012 ∙ by John Moeller, et al. ∙ 0 ∙ share

read it
Efficient Protocols for Distributed Classification and Optimization

In distributed learning, the goal is to perform a learning task over dat...
04/16/2012 ∙ by Hal Daumé III, et al. ∙ 0 ∙ share

read it
Protocols for Learning Classifiers on Distributed Data

We consider the problem of learning classifiers for labeled data that ha...
02/27/2012 ∙ by Hal Daumé III, et al. ∙ 0 ∙ share

read it
A Unified Algorithmic Framework for Multi-Dimensional Scaling

In this paper, we propose a unified algorithmic framework for solving ma...
03/02/2010 ∙ by Arvind Agarwal, et al. ∙ 0 ∙ share

read it
Comparing Distributions and Shapes using the Kernel Distance

Starting with a similarity function between objects, it is possible to d...
01/04/2010 ∙ by Sarang Joshi, et al. ∙ 0 ∙ share

read it
The Graphics Card as a Streaming Computer

Massive data sets have radically changed our understanding of how to des...
10/05/2003 ∙ by Suresh Venkatasubramanian, et al. ∙ 0 ∙ share

read it

Suresh Venkatasubramanian

Featured Co-authors

Hal Daumé III

Jeff M. Phillips

Robert M. Kirby

Carlos Scheidegger

Vivek Gupta

Valerio Pascucci

Sorelle A. Friedler

Aditya Bhaskara

Miriah Meyer

Daniel Lopresti

Sarang Joshi

A Research Ecosystem for Secure Computing

Interdisciplinary Approaches to Understanding Artificial Intelligence's Impact on Society

Evolving Methods for Evaluating and Disseminating Computing Research

Fair clustering via equitable group representations

Problems with Shapley-value-based explanations as feature importance measures

Equalizing Recourse across Groups

Disentangling Influence: Using Disentangled Representations to Audit Model Predictions

Fairness in representation: quantifying stereotyping as a representational harm

Sublinear Algorithms for MAXCUT and Correlation Clustering

A comparative study of fairness-enhancing interventions in machine learning

Fair Pipelines

Runaway Feedback Loops in Predictive Policing

On the (im)possibility of fairness

A Unified View of Localized Kernel Learning

Auditing Black-box Models for Indirect Influence

A Group Theoretic Perspective on Unsupervised Deep Learning

Why does Deep Learning work? - A perspective from Group Theory

Certifying and removing disparate impact

Rethinking Abstractions for Big Data: Why, Where, How, and What

A Geometric Algorithm for Scalable Multiple Kernel Learning

Efficient Protocols for Distributed Classification and Optimization

Protocols for Learning Classifiers on Distributed Data

A Unified Algorithmic Framework for Multi-Dimensional Scaling

Comparing Distributions and Shapes using the Kernel Distance

The Graphics Card as a Streaming Computer