All reports by Author Miklos Santha:

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TR17-107
| 1st June 2017
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Anurag Anshu, Dmytro Gavinsky, Rahul Jain, Srijita Kundu, Troy Lee, Priyanka Mukhopadhyay, Miklos Santha, Swagato Sanyal#### A Composition Theorem for Randomized Query complexity

Revisions: 1

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TR16-072
| 4th May 2016
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Anurag Anshu, Aleksandrs Belovs, Shalev Ben-David, Mika G\"o{\"o}s, Rahul Jain, Robin Kothari, Troy Lee, Miklos Santha#### Separations in communication complexity using cheat sheets and information complexity

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TR15-098
| 15th June 2015
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Andris Ambainis, Kaspars Balodis, Aleksandrs Belovs, Troy Lee, Miklos Santha, Juris Smotrovs#### Separations in Query Complexity Based on Pointer Functions

Revisions: 2

Anurag Anshu, Dmytro Gavinsky, Rahul Jain, Srijita Kundu, Troy Lee, Priyanka Mukhopadhyay, Miklos Santha, Swagato Sanyal

Let the randomized query complexity of a relation for error probability $\epsilon$ be denoted by $\R_\epsilon(\cdot)$. We prove that for any relation $f \subseteq \{0,1\}^n \times \mathcal{R}$ and Boolean function $g:\{0,1\}^m \rightarrow \{0,1\}$, $\R_{1/3}(f\circ g^n) = \Omega(\R_{4/9}(f)\cdot\R_{1/2-1/n^4}(g))$, where $f \circ g^n$ is the relation obtained by composing $f$ and $g$. ... more >>>

Anurag Anshu, Aleksandrs Belovs, Shalev Ben-David, Mika G\"o{\"o}s, Rahul Jain, Robin Kothari, Troy Lee, Miklos Santha

While exponential separations are known between quantum and randomized communication complexity for partial functions, e.g. Raz [1999], the best known separation between these measures for a total function is quadratic, witnessed by the disjointness function. We give the first super-quadratic separation between quantum and randomized

communication complexity for a ...
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Andris Ambainis, Kaspars Balodis, Aleksandrs Belovs, Troy Lee, Miklos Santha, Juris Smotrovs

In 1986, Saks and Wigderson conjectured that the largest separation between deterministic and zero-error randomized

query complexity for a total boolean function is given by the function $f$ on $n=2^k$ bits defined by a complete binary tree

of NAND gates of depth $k$, which achieves $R_0(f) = O(D(f)^{0.7537\ldots})$. ...
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