1.5k citations · 5.7k across the 60 of their papers we have counts for
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A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians
Aleksander Mądry, Slobodan Mitrović, Ludwig Schmidt
Sparsity-based methods are widely used in machine learning, statistics, and signal processing. There is now a rich class of structured sparsity approaches that expand the modeling…
Exploring the Landscape of Spatial Robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras +2
The study of adversarial robustness has so far largely focused on perturbations bound in p-norms. However, state-of-the-art models turn out to be also vulnerable to other, more nat…
k-server via multiscale entropic regularization
Sebastien Bubeck, Michael B. Cohen, James R. Lee +2
We present an -competitive randomized algorithm for the -server problem on hierarchically separated trees (HSTs). This is the first -competitive randomized…
A Classification-Based Study of Covariate Shift in GAN Distributions
Shibani Santurkar, Ludwig Schmidt, Aleksander Mądry
A basic, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether they are truly able to capture all the fundamental characteristi…
Round Compression for Parallel Matching Algorithms
Artur Czumaj, Jakub Łącki, Aleksander Mądry +3
For over a decade now we have been witnessing the success of {\em massive parallel computation} (MPC) frameworks, such as MapReduce, Hadoop, Dryad, or Spark. One of the reasons for…
On the Limitations of First-Order Approximation in GAN Dynamics
Jerry Li, Aleksander Madry, John Peebles +1
While Generative Adversarial Networks (GANs) have demonstrated promising performance on multiple vision tasks, their learning dynamics are not yet well understood, both in theory a…