163 citations · 256 across the 13 of their papers we have counts for
7 papers · 1 filter
Provable Robustness for Streaming Models with a Sliding Window
Aounon Kumar, Vinu Sankar Sadasivan, Soheil Feizi
The literature on provable robustness in machine learning has primarily focused on static prediction problems, such as image classification, in which input samples are assumed to b…
Certifying Model Accuracy under Distribution Shifts
Aounon Kumar, Alexander Levine, Tom Goldstein +1
Certified robustness in machine learning has primarily focused on adversarial perturbations of the input with a fixed attack budget for each point in the data distribution. In this…
Policy Smoothing for Provably Robust Reinforcement Learning
Aounon Kumar, Alexander Levine, Soheil Feizi
The study of provable adversarial robustness for deep neural networks (DNNs) has mainly focused on static supervised learning tasks such as image classification. However, DNNs have…
Center Smoothing: Certified Robustness for Networks with Structured Outputs
Aounon Kumar, Tom Goldstein
The study of provable adversarial robustness has mostly been limited to classification tasks and models with one-dimensional real-valued outputs. We extend the scope of certifiable…
Certifying Confidence via Randomized Smoothing
Aounon Kumar, Alexander Levine, Soheil Feizi +1
Randomized smoothing has been shown to provide good certified-robustness guarantees for high-dimensional classification problems. It uses the probabilities of predicting the top tw…
Tight Second-Order Certificates for Randomized Smoothing
Alexander Levine, Aounon Kumar, Thomas Goldstein +1
Randomized smoothing is a popular way of providing robustness guarantees against adversarial attacks: randomly-smoothed functions have a universal Lipschitz-like bound, allowing fo…
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar, Alexander Levine, Tom Goldstein +1
Randomized smoothing, using just a simple isotropic Gaussian distribution, has been shown to produce good robustness guarantees against -norm bounded adversaries. In this w…