16 citations · 66 across the 13 of their papers we have counts for
13 papers
On the sample complexity of parameter estimation in logistic regression with normal design
Daniel Hsu, Arya Mazumdar
The logistic regression model is one of the most popular data generation model in noisy binary classification problems. In this work, we study the sample complexity of estimating t…
Representational Strengths and Limitations of Transformers
Clayton Sanford, Daniel Hsu, Matus Telgarsky
Attention layers, as commonly used in transformers, form the backbone of modern deep learning, yet there is no mathematical description of their benefits and deficiencies as compar…
Intrinsic dimensionality and generalization properties of the -norm inductive bias
Navid Ardeshir, Daniel Hsu, Clayton Sanford
We study the structural and statistical properties of -norm minimizing interpolants of datasets labeled by specific target functions. The -norm is the bas…
Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity
Rishabh Dudeja, Daniel Hsu
Tensor PCA is a stylized statistical inference problem introduced by Montanari and Richard to study the computational difficulty of estimating an unknown parameter from higher-orde…
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…