80 citations · 138 across the 20 of their papers we have counts for
17 papers · 1 filter
Reverse Engineering attacks: A block-sparse optimization approach with recovery guarantees
Darshan Thaker, Paris Giampouras, René Vidal
Deep neural network-based classifiers have been shown to be vulnerable to imperceptible perturbations to their input, such as -bounded norm adversarial attacks. This has mo…
Doubly Stochastic Subspace Clustering
Derek Lim, René Vidal, Benjamin D. Haeffele
Many state-of-the-art subspace clustering methods follow a two-step process by first constructing an affinity matrix between data points and then applying spectral clustering to th…
A Critique of Self-Expressive Deep Subspace Clustering
Benjamin D. Haeffele, Chong You, René Vidal
Subspace clustering is an unsupervised clustering technique designed to cluster data that is supported on a union of linear subspaces, with each subspace defining a cluster with di…
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Ambar Pal, René Vidal
Research in adversarial learning follows a cat and mouse game between attackers and defenders where attacks are proposed, they are mitigated by new defenses, and subsequently new a…
Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces
Chong You, Chi Li, Daniel P. Robinson +1
Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classi…
Is an Affine Constraint Needed for Affine Subspace Clustering?
Chong You, Chun-Guang Li, Daniel P. Robinson +1
Subspace clustering methods based on expressing each data point as a linear combination of other data points have achieved great success in computer vision applications such as mot…