Zobrazeno 1 - 10
of 391
pro vyhledávání: '"Zuckerman, David"'
Autor:
Xun, Zhiyang, Zuckerman, David
We present the first efficient averaging sampler that achieves asymptotically optimal randomness complexity and near-optimal sample complexity. For any $\delta < \varepsilon$ and any constant $\alpha > 0$, our sampler uses $m + O(\log (1 / \delta))$
Externí odkaz:
http://arxiv.org/abs/2411.10870
One of the earliest models of weak randomness is the Chor-Goldreich (CG) source. A $(t,n,k)$-CG source is a sequence of random variables $X=(X_1,\dots,X_t)\sim(\{0,1\}^n)^t$, where each $X_i$ has min-entropy $k$ conditioned on any fixing of $X_1,\dot
Externí odkaz:
http://arxiv.org/abs/2410.08142
We construct explicit deterministic extractors for polynomial images of varieties, that is, distributions sampled by applying a low-degree polynomial map $f : \mathbb{F}_q^r \to \mathbb{F}_q^n$ to an element sampled uniformly at random from a $k$-dim
Externí odkaz:
http://arxiv.org/abs/2211.14497
Autor:
Merckling, Matthew, Koltenyuk, Victor, Zuckerman, David, Hayes, Brianna, Rafieezadeh, Aryan, Zangbar, Bardiya, Patel, Harshadkumar, Tyagi, Rachana
Publikováno v:
In Journal of Pediatric Surgery Open October 2024 8
Autor:
Beaudreault, Cameron P., Chiang, Sharon, Sacknovitz, Ariel, Moss, Robert, Brabant, Paige, Zuckerman, David, Dorilio, Jessica R., Spirollari, Eris, Naftchi, Alexandria F., McGoldrick, Patricia E., Muh, Carrie R., Wang, Richard, Nolan, Bridget, Clare, Kevin, Sukul, Vishad V., Wolf, Steven M.
Publikováno v:
In Epilepsy & Behavior October 2024 159
Autor:
Zuckerman, David
Publikováno v:
In Journal of Economic Behavior and Organization February 2024 218:486-513
We give a deterministic, nearly logarithmic-space algorithm for mild spectral sparsification of undirected graphs. Given a weighted, undirected graph $G$ on $n$ vertices described by a binary string of length $N$, an integer $k\leq \log n$, and an er
Externí odkaz:
http://arxiv.org/abs/2002.11237
The seminal result of Kahn, Kalai and Linial shows that a coalition of $O(\frac{n}{\log n})$ players can bias the outcome of any Boolean function $\{0,1\}^n \to \{0,1\}$ with respect to the uniform measure. We extend their result to arbitrary product
Externí odkaz:
http://arxiv.org/abs/1902.07426
Akademický článek
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