16 citations · 18 across the 3 of their papers we have counts for
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cs.LG2022★ 2 cited
A Theory of PAC Learnability under Transformation Invariances
Han Shao, Omar Montasser, Avrim Blum
Transformation invariances are present in many real-world problems. For example, image classification is usually invariant to rotation and color transformation: a rotated car in a…
cs.LG2017★ 16 cited
The Riemannian Geometry of Deep Generative Models
Hang Shao, Abhishek Kumar, P. Thomas Fletcher
Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space. Under certain regularity conditions, these models parameterize nonlinea…