69 citations · 336 across the 41 of their papers we have counts for
44 papers
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria
Nataly Brukhim, Amit Daniely, Yishay Mansour +1
We study a generalization of boosting to the multiclass setting. We introduce a weak learning condition for multiclass classification that captures the original notion of weak lear…
Most Neural Networks Are Almost Learnable
Amit Daniely, Nathan Srebro, Gal Vardi
We present a PTAS for learning random constant-depth networks. We show that for any fixed and depth , there is a poly-time algorithm that for any distribution on $\sqrt{d}…
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
Amit Daniely, Nathan Srebro, Gal Vardi
Understanding when neural networks can be learned efficiently is a fundamental question in learning theory. Existing hardness results suggest that assumptions on both the input dis…
On the Sample Complexity of Two-Layer Networks: Lipschitz vs. Element-Wise Lipschitz Activation
Amit Daniely, Elad Granot
We investigate the sample complexity of bounded two-layer neural networks using different activation functions. In particular, we consider the class $$ \mathcal{H} = \left\{\textbf…
Approximate Description Length, Covering Numbers, and VC Dimension
Amit Daniely, Gal Katzhendler
Recently, Daniely and Granot [arXiv:1910.05697] introduced a new notion of complexity called Approximate Description Length (ADL). They used it to derive novel generalization bound…
Monotone Learning
Olivier Bousquet, Amit Daniely, Haim Kaplan +3
The amount of training-data is one of the key factors which determines the generalization capacity of learning algorithms. Intuitively, one expects the error rate to decrease as th…