Search Results for author:
Found 7 papers, 4 papers with code
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
As an application of our algorithm, we evaluate two large conditional generative models that are pre-trained on the CelebA dataset by analyzing the strength of spurious correlations and the level of disentanglement they achieve.
Zonotope Domains for Lagrangian Neural Network Verification
Neural network verification aims to provide provable bounds for the output of a neural network for a given input range.
Inverse Problems Leveraging Pre-trained Contrastive Representations
The problem is to recover the representation of an image R(x), if we are only given a corrupted version A(x), for some known forward operator A.
Provable Lipschitz Certification for Generative Models
We present a scalable technique for upper bounding the Lipschitz constant of generative models.
Exactly Computing the Local Lipschitz Constant of ReLU Networks
The local Lipschitz constant of a neural network is a useful metric with applications in robustness, generalization, and fairness evaluation.
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
We relate the problem of computing pointwise robustness of these networks to that of computing the maximum norm ball with a fixed center that can be contained in a non-convex polytope.
Quantifying Perceptual Distortion of Adversarial Examples
To demonstrate the value of quantifying the perceptual distortion of adversarial examples, we present and employ a unifying framework fusing different attack styles.
