Matt Jordan

I am fourth-year PhD student in the department of Computer Science at the University of Texas at Austin advised by Alex Dimakis . I am broadly interested in the theoretical underpinnings of machine learning with guarantees, focusing on adversarial robustness and certification. I am also interested in ReLU geometry and algorithmic fairness. Prior to this, I received my bachelors degree form MIT majoring in computer science and theoretical mathematics.

Publications

	Provable Lipschitz Certification for Generative Models Matt Jordan, Alexandros G. Dimakis International Conference on Machine Learning (ICML) 2021. [PDF]   [Code]
	Quarantines as a Targeted Immunization Strategy Jessica Hoffmann, Matt Jordan, Constantine Caramanis. Preprint, arXiv:2008.08262. [PDF]
	Exactly Computing the Local Lipschitz Constant of ReLU Networks Matt Jordan, Alexandros G. Dimakis. Advances in Neural Information Processing Systems (NeurIPS) 2020. [PDF]   [Code]
	Provable Certificates for Adversarial Examples: Fitting a Ball in a Union of Polytopes Matt Jordan, Justin Lewis, Alexandros G. Dimakis. Advances in Neural Information Processing Systems (NeurIPS) 2019. [PDF]   [Code]   [Talk]
	Quantifying Perceptual Distortion of Adversarial Examples Matt Jordan, Naren Manoj, Surbhi Goel, Alexandros G. Dimakis. Preprint, arXiv:1902.08265. [PDF]   [Code]