69 citations · 336 across the 41 of their papers we have counts for
6 papers · 1 filter
Neural Networks Learning and Memorization with (almost) no Over-Parameterization
Amit Daniely
Many results in recent years established polynomial time learnability of various models via neural networks algorithms. However, unless the model is linear separable, or the activa…
Generalization Bounds for Neural Networks via Approximate Description Length
Amit Daniely, Elad Granot
We investigate the sample complexity of networks with bounds on the magnitude of its weights. In particular, we consider the class \[ H=\left\{W_t\circρ\circ \ldots\circρ\circ W_{1…
The Implicit Bias of Depth: How Incremental Learning Drives Generalization
Daniel Gissin, Shai Shalev-Shwartz, Amit Daniely
A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the…
On the Optimality of Trees Generated by ID3
Alon Brutzkus, Amit Daniely, Eran Malach
Since its inception in the 1980s, ID3 has become one of the most successful and widely used algorithms for learning decision trees. However, its theoretical properties remain poorl…
ID3 Learns Juntas for Smoothed Product Distributions
Alon Brutzkus, Amit Daniely, Eran Malach
In recent years, there are many attempts to understand popular heuristics. An example of such a heuristic algorithm is the ID3 algorithm for learning decision trees. This algorithm…
Competitive ratio versus regret minimization: achieving the best of both worlds
Amit Daniely, Yishay Mansour
We consider online algorithms under both the competitive ratio criteria and the regret minimization one. Our main goal is to build a unified methodology that would be able to guara…