All reports by Author Ofer Grossman:

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TR19-072
| 17th May 2019
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Lijie Chen, Ofer Grossman#### Broadcast Congested Clique: Planted Cliques and Pseudorandom Generators

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TR18-048
| 11th March 2018
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Ofer Grossman, Yang P. Liu#### Reproducibility and Pseudo-Determinism in Log-Space

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TR17-105
| 14th June 2017
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Shafi Goldwasser, Ofer Grossman, Dhiraj Holden#### Pseudo-Deterministic Proofs

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TR15-208
| 26th December 2015
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Shafi Goldwasser, Ofer Grossman#### Perfect Bipartite Matching in Pseudo-Deterministic $RNC$

Revisions: 2

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TR15-207
| 23rd December 2015
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Ofer Grossman#### Finding Primitive Roots Pseudo-Deterministically

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TR15-158
| 27th September 2015
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Ofer Grossman, Dana Moshkovitz#### Amplification and Derandomization Without Slowdown

Lijie Chen, Ofer Grossman

Consider the multiparty communication complexity model where there are n processors, each receiving as input a row of an n by n matrix M with entries in {0, 1}, and in each round each party can broadcast a single bit to all other parties (this is known as the BCAST(1) ... more >>>

Ofer Grossman, Yang P. Liu

A curious property of randomized log-space search algorithms is that their outputs are often longer than their workspace. This leads to the question: how can we reproduce the results of a randomized log space computation without storing the output or randomness verbatim? Running the algorithm again with new random bits ... more >>>

Shafi Goldwasser, Ofer Grossman, Dhiraj Holden

We introduce pseudo-deterministic interactive proofs (psdAM): interactive proof systems for search problems where

the verifier is guaranteed with high probability to output the same output on different executions.

As in the case with classical interactive proofs,

the verifier is a probabilistic polynomial time algorithm interacting with an untrusted powerful prover.

Shafi Goldwasser, Ofer Grossman

In this paper we present a pseudo-deterministic $RNC$ algorithm for finding perfect matchings in bipartite graphs. Specifically, our algorithm is a randomized parallel algorithm which uses $poly(n)$ processors, $poly({\log n})$ depth, $poly(\log n)$ random bits, and outputs for each bipartite input graph a unique perfect matching with high probability. That ... more >>>

Ofer Grossman

Pseudo-deterministic algorithms are randomized search algorithms which output unique solutions (i.e., with high probability they output the same solution on each execution). We present a pseudo-deterministic algorithm that, given a prime $p,$ finds a primitive root modulo $p$ in time $\exp(O(\sqrt{\log p \log \log p}))$. This improves upon the previous ... more >>>

Ofer Grossman, Dana Moshkovitz

We present techniques for decreasing the error probability of randomized algorithms and for converting randomized algorithms to deterministic (non-uniform) algorithms. Unlike most existing techniques that involve repetition of the randomized algorithm, and hence a slowdown, our techniques produce algorithms with a similar run-time to the original randomized algorithms.

The ... more >>>