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20172020
most citedOn Adversarial Bias and the Robustness of Fair Machine Learning

37 citations · 102 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.LG2020

Trace-Norm Adversarial Examples

Ehsan Kazemi, Thomas Kerdreux, Liqiang Wang

White box adversarial perturbations are sought via iterative optimization algorithms most often minimizing an adversarial loss on a neighborhood of the original image, the so…

cs.LG20203 cited

Submodular Maximization Through Barrier Functions

Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi +1

In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves t…

cs.LG202016 cited

Regularized Submodular Maximization at Scale

Ehsan Kazemi, Shervin Minaee, Moran Feldman +1

In this paper, we propose scalable methods for maximizing a regularized submodular function expressed as the difference between a monotone submodular function an…

cs.LG201932 cited

Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity

Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam +2

Streaming algorithms are generally judged by the quality of their solution, memory footprint, and computational complexity. In this paper, we study the problem of maximizing a mono…

cs.LG20195 cited

Adaptive Sequence Submodularity

Marko Mitrovic, Ehsan Kazemi, Moran Feldman +2

In many machine learning applications, one needs to interactively select a sequence of items (e.g., recommending movies based on a user's feedback) or make sequential decisions in…

cs.LG20173 cited

Deletion-Robust Submodular Maximization at Scale

Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi

Can we efficiently extract useful information from a large user-generated dataset while protecting the privacy of the users and/or ensuring fairness in representation. We cast this…