42 citations · 204 across the 20 of their papers we have counts for
12 papers · 1 filter
Complementing Semi-Supervised Learning with Uncertainty Quantification
Ehsan Kazemi
The problem of fully supervised classification is that it requires a tremendous amount of annotated data, however, in many datasets a large portion of data is unlabeled. To allevia…
Generating Structured Adversarial Attacks Using Frank-Wolfe Method
Ehsan Kazemi, Thomas Kerdreux, Liquang Wang
White box adversarial perturbations are generated via iterative optimization algorithms most often by minimizing an adversarial loss on a neighborhood of the original imag…
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…
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…
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…
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…