31 citations · 83 across the 9 of their papers we have counts for
5 papers · 1 filter
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
Md Musfiqur Rahman, Matt Jordan, Murat Kocaoglu
Causal inference from observational data plays critical role in many applications in trustworthy machine learning. While sound and complete algorithms exist to compute causal effec…
Zonotope Domains for Lagrangian Neural Network Verification
Matt Jordan, Jonathan Hayase, Alexandros G. Dimakis +1
Neural network verification aims to provide provable bounds for the output of a neural network for a given input range. Notable prior works in this domain have either generated bou…
Inverse Problems Leveraging Pre-trained Contrastive Representations
Sriram Ravula, Georgios Smyrnis, Matt Jordan +1
We study a new family of inverse problems for recovering representations of corrupted data. We assume access to a pre-trained representation learning network R(x) that operates on…
Provable Lipschitz Certification for Generative Models
Matt Jordan, Alexandros G. Dimakis
We present a scalable technique for upper bounding the Lipschitz constant of generative models. We relate this quantity to the maximal norm over the set of attainable vector-Jacobi…
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
Matt Jordan, Justin Lewis, Alexandros G. Dimakis
We propose a novel method for computing exact pointwise robustness of deep neural networks for all convex norms. Our algorithm, GeoCert, finds the largest ball ce…