125 citations · 346 across the 41 of their papers we have counts for
3 papers · 1 filter
On the Power of Differentiable Learning versus PAC and SQ Learning
Emmanuel Abbe, Pritish Kamath, Eran Malach +2
We study the power of learning via mini-batch stochastic gradient descent (SGD) on the population loss, and batch Gradient Descent (GD) on the empirical loss, of a differentiable m…
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
Eran Malach, Pritish Kamath, Emmanuel Abbe +1
We study the relative power of learning with gradient descent on differentiable models, such as neural networks, versus using the corresponding tangent kernels. We show that under…
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz +1
Several recent works have shown separation results between deep neural networks, and hypothesis classes with inferior approximation capacity such as shallow networks or kernel clas…