All reports by Author Shay Moran:

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TR17-177
| 16th November 2017
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Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff#### On Communication Complexity of Classification Problems

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TR17-085
| 4th May 2017
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Daniel Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang#### Active classification with comparison queries

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TR17-082
| 4th May 2017
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Daniel Kane, Shachar Lovett, Shay Moran#### Near-optimal linear decision trees for k-SUM and related problems

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TR15-189
| 25th November 2015
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Shay Moran, Cyrus Rashtchian#### Shattered Sets and the Hilbert Function

Revisions: 1

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TR15-040
| 24th March 2015
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Shay Moran, Amir Yehudayoff#### Proper PAC learning is compressing

Revisions: 1

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TR15-025
| 22nd February 2015
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Shay Moran, Amir Shpilka, Avi Wigderson, Amir Yehudayoff#### Teaching and compressing for low VC-dimension

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TR14-135
| 23rd October 2014
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Noga Alon, Shay Moran, Amir Yehudayoff#### Sign rank, VC dimension and spectral gaps

Revisions: 2

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TR14-101
| 8th August 2014
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Balthazar Bauer, Shay Moran, Amir Yehudayoff#### Internal compression of protocols to entropy

Revisions: 1

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TR14-046
| 8th April 2014
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Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff#### Approximate Nonnegative Rank is Equivalent to the Smooth Rectangle Bound

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TR14-022
| 19th February 2014
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Shay Moran, Makrand Sinha, Amir Yehudayoff#### Fooling Pairs in Randomized Communication Complexity

Revisions: 1

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TR13-106
| 29th July 2013
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Shay Moran, Amir Yehudayoff#### A note on average-case sorting

Revisions: 2

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TR13-079
| 2nd June 2013
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Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff#### Direct Sum Fails for Zero Error Average Communication

Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff

This work introduces a model of distributed learning in the spirit of Yao's communication complexity model. We consider a two-party setting, where each of the players gets a list of labelled examples and they communicate in order to jointly perform some learning task. To naturally fit into the framework of ... more >>>

Daniel Kane, Shachar Lovett, Shay Moran, Jiapeng Zhang

We study an extension of active learning in which the learning algorithm may ask the annotator to compare the distances of two examples from the boundary of their label-class. For example, in a recommendation system application (say for restaurants), the annotator may be asked whether she liked or disliked a ... more >>>

Daniel Kane, Shachar Lovett, Shay Moran

We construct near optimal linear decision trees for a variety of decision problems in combinatorics and discrete geometry.

For example, for any constant $k$, we construct linear decision trees that solve the $k$-SUM problem on $n$ elements using $O(n \log^2 n)$ linear queries.

Moreover, the queries we use are comparison ...
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Shay Moran, Cyrus Rashtchian

We study complexity measures on subsets of the boolean hypercube and exhibit connections between algebra (the Hilbert function) and combinatorics (VC theory). These connections yield results in both directions. Our main complexity-theoretic result proves that most linear program feasibility problems cannot be computed by polynomial-sized constant-depth circuits. Moreover, our result ... more >>>

Shay Moran, Amir Yehudayoff

We prove that proper PAC learnability implies compression. Namely, if a concept $C \subseteq \Sigma^X$ is properly PAC learnable with $d$ samples, then $C$ has a sample compression scheme of size $2^{O(d)}$.

In particular, every boolean concept class with constant VC dimension has a sample compression scheme of constant size. ...
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Shay Moran, Amir Shpilka, Avi Wigderson, Amir Yehudayoff

In this work we study the quantitative relation between VC-dimension and two other basic parameters related to learning and teaching. We present relatively efficient constructions of {\em sample compression schemes} and

for classes of low VC-dimension. Let $C$ be a finite boolean concept class of VC-dimension $d$. Set $k ...
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Noga Alon, Shay Moran, Amir Yehudayoff

We study the maximum possible sign rank of $N \times N$ sign matrices with a given VC dimension $d$. For $d=1$, this maximum is $3$. For $d=2$, this maximum is $\tilde{\Theta}(N^{1/2})$. Similar (slightly less accurate) statements hold for $d>2$ as well. We discuss the tightness of our methods, and describe ... more >>>

Balthazar Bauer, Shay Moran, Amir Yehudayoff

We study internal compression of communication protocols

to their internal entropy, which is the entropy of the transcript from the players' perspective.

We first show that errorless compression to the internal entropy

(and hence to the internal information) is impossible.

We then provide two internal compression schemes with error.

One ...
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Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff

We consider two known lower bounds on randomized communication complexity: The smooth rectangle bound and the logarithm of the approximate non-negative rank. Our main result is that they are the same up to a multiplicative constant and a small additive term.

The logarithm of the nonnegative rank is known to ...
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Shay Moran, Makrand Sinha, Amir Yehudayoff

Fooling pairs are one of the standard methods for proving lower bounds for deterministic two-player communication complexity. We study fooling pairs in the context of randomized communication complexity.

We show that every fooling pair induces far away distributions on transcripts of private-coin protocols. We then conclude that the private-coin randomized ...
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Shay Moran, Amir Yehudayoff

This note studies the average-case comparison-complexity of sorting n elements when there is a known distribution on inputs and the goal is to minimize

the expected number of comparisons. We generalize Fredman's algorithm which

is a variant of insertion sort and provide a basically tight upper bound: If \mu is

more >>>

Gillat Kol, Shay Moran, Amir Shpilka, Amir Yehudayoff

We show that in the model of zero error communication complexity, direct sum fails for average communication complexity as well as for external information cost. Our example also refutes a version of a conjecture by Braverman et al. that in the zero error case amortized communication complexity equals external information ... more >>>