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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > INFLUENCE:
Reports tagged with influence:
TR11-033 | 8th March 2011
Rahul Jain, Shengyu Zhang

The influence lower bound via query elimination

We give a simpler proof, via query elimination, of a result due to O'Donnell, Saks, Schramm and Servedio, which shows a lower bound on the zero-error randomized query complexity of a function $f$ in terms of the maximum influence of any variable of $f$. Our lower bound also applies to ... more >>>


TR19-166 | 20th November 2019
Guy Blanc, Jane Lange, Li-Yang Tan

Top-down induction of decision trees: rigorous guarantees and inherent limitations

Consider the following heuristic for building a decision tree for a function $f : \{0,1\}^n \to \{\pm 1\}$. Place the most influential variable $x_i$ of $f$ at the root, and recurse on the subfunctions $f_{x_i=0}$ and $f_{x_i=1}$ on the left and right subtrees respectively; terminate once the tree is an ... more >>>


TR21-111 | 19th July 2021
Aniruddha Biswas, Palash Sarkar

Influence of a Set of Variables on a Boolean Function

Revisions: 2

The influence of a set of variables on a Boolean function has three separate definitions in the literature, the first due to Ben-Or and Linial (1989), the second due to Fischer et al. (2002) and Blais (2009) and the third due to Tal (2017). The goal of the present work ... more >>>


TR24-171 | 6th November 2024
Eshan Chattopadhyay, Mohit Gurumukhani, Noam Ringach

Condensing against Online Adversaries

We investigate the task of deterministically condensing randomness from Online Non-Oblivious Symbol Fixing (oNOSF) sources, a natural model of defective random sources for which it is known that extraction is impossible [AORSV, EUROCRYPT'20]. A $(g,\ell)$-oNOSF source is a sequence of $\ell$ blocks $\mathbf{X} = (\mathbf{X}_1, \dots, \mathbf{X}_{\ell})\sim (\{0, 1\}^{n})^{\ell}$, where ... more >>>




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