14 citations · 22 across the 11 of their papers we have counts for
5 papers · 1 filter
Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks
Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi
The reliable deployment of neural networks in control systems requires rigorous robustness guarantees. In this paper, we obtain tight robustness certificates over convex attack set…
Projected Randomized Smoothing for Certified Adversarial Robustness
Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
Randomized smoothing is the current state-of-the-art method for producing provably robust classifiers. While randomized smoothing typically yields robust -ball certificates…
Initial State Interventions for Deconfounded Imitation Learning
Samuel Pfrommer, Yatong Bai, Hyunin Lee +1
Imitation learning suffers from causal confusion. This phenomenon occurs when learned policies attend to features that do not causally influence the expert actions but are instead…
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization
Tanmay Gautam, Samuel Pfrommer, Somayeh Sojoudi
Conventional optimization methods in machine learning and controls rely heavily on first-order update rules. Selecting the right method and hyperparameters for a particular task of…
Asymmetric Certified Robustness via Feature-Convex Neural Networks
Samuel Pfrommer, Brendon G. Anderson, Julien Piet +1
Recent works have introduced input-convex neural networks (ICNNs) as learning models with advantageous training, inference, and generalization properties linked to their convex str…