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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > DIMENSION REDUCTION:
Reports tagged with dimension reduction:
TR10-183 | 29th November 2010
Raghu Meka

Almost Optimal Explicit Johnson-Lindenstrauss Transformations

Revisions: 2

The Johnson-Lindenstrauss lemma is a fundamental result in probability with several applications in the design and analysis of algorithms in high dimensional geometry. Most known constructions of linear embeddings that satisfy the Johnson-Lindenstrauss property involve randomness. We address the question of explicitly constructing such embedding families and provide a construction ... more >>>


TR12-076 | 12th June 2012
Pranjal Awasthi, Madhav Jha, Marco Molinaro, Sofya Raskhodnikova

Testing Lipschitz Functions on Hypergrid Domains

A function $f(x_1, ... , x_d)$, where each input is an integer from 1 to $n$ and output is a real number, is Lipschitz if changing one of the inputs by 1 changes the output by at most 1. In other words, Lipschitz functions are not very sensitive to small ... more >>>


TR17-125 | 7th August 2017
Badih Ghazi, Pritish Kamath, Prasad Raghavendra

Dimension Reduction for Polynomials over Gaussian Space and Applications

In this work we introduce a new technique for reducing the dimension of the ambient space of low-degree polynomials in the Gaussian space while preserving their relative correlation structure. As applications, we address the following problems:

(I) Computability of the Approximately Optimal Noise Stable function over Gaussian space:

The goal ... more >>>


TR21-067 | 6th May 2021
Zeyu Guo

Variety Evasive Subspace Families

We introduce the problem of constructing explicit variety evasive subspace families. Given a family $\mathcal{F}$ of subvarieties of a projective or affine space, a collection $\mathcal{H}$ of projective or affine $k$-subspaces is $(\mathcal{F},\epsilon)$-evasive if for every $\mathcal{V}\in\mathcal{F}$, all but at most $\epsilon$-fraction of $W\in\mathcal{H}$ intersect every irreducible component of $\mathcal{V}$ ... more >>>




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