activity
20152019
most citedDepth Separation for Neural Networks

17 citations · 33 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG2019

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…

cs.LG20177 cited

Random Features for Compositional Kernels

Amit Daniely, Roy Frostig, Vineet Gupta +1

We describe and analyze a simple random feature scheme (RFS) from prescribed compositional kernels. The compositional kernels we use are inspired by the structure of convolutional…

cs.LG201717 cited

Depth Separation for Neural Networks

Amit Daniely

Let be a function of the form for $g:[-1,1]\to \mathb…

cs.LG2016

Sketching and Neural Networks

Amit Daniely, Nevena Lazic, Yoram Singer +1

High-dimensional sparse data present computational and statistical challenges for supervised learning. We propose compact linear sketches for reducing the dimensionality of the inp…

cs.LG2016

Distribution Free Learning with Local Queries

Galit Bary-Weisberg, Amit Daniely, Shai Shalev-Shwartz

The model of learning with \emph{local membership queries} interpolates between the PAC model and the membership queries model by allowing the learner to query the label of any exa…

cs.LG20159 cited

Strongly Adaptive Online Learning

Amit Daniely, Alon Gonen, Shai Shalev-Shwartz

Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms t…