14 citations · 26 across the 5 of their papers we have counts for
5 papers · 1 filter
Masked prediction tasks: a parameter identifiability view
Bingbin Liu, Daniel Hsu, Pradeep Ravikumar +1
The vast majority of work in self-supervised learning, both theoretical and empirical (though mostly the latter), have largely focused on recovering good features for downstream ta…
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals
Daniel Hsu, Clayton Sanford, Rocco Servedio +1
We consider the well-studied problem of learning intersections of halfspaces under the Gaussian distribution in the challenging \emph{agnostic learning} model. Recent work of Diako…
Generalization bounds via distillation
Daniel Hsu, Ziwei Ji, Matus Telgarsky +1
This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network with ne…
On the Approximation Power of Two-Layer Networks of Random ReLUs
Daniel Hsu, Clayton Sanford, Rocco A. Servedio +1
This paper considers the following question: how well can depth-two ReLU networks with randomly initialized bottom-level weights represent smooth functions? We give near-matching u…
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang +3
This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The app…