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of 296
pro vyhledávání: '"Charikar, Moses"'
A cornerstone of social choice theory is Condorcet's paradox which says that in an election where $n$ voters rank $m$ candidates it is possible that, no matter which candidate is declared the winner, a majority of voters would have preferred an alter
Externí odkaz:
http://arxiv.org/abs/2411.03390
Autor:
Amrollahi, Daneshvar, Preiner, Mathias, Niemetz, Aina, Reynolds, Andrew, Charikar, Moses, Tinelli, Cesare, Barrett, Clark
In many applications, SMT solvers are used to solve similar or identical tasks over time. When the performance of the solver varies significantly despite only small changes, this leads to frustration for users. This has been called the stability prob
Externí odkaz:
http://arxiv.org/abs/2410.22419
Recent advances in large language models have shown capabilities that are extraordinary and near-superhuman. These models operate with such complexity that reliably evaluating and aligning them proves challenging for humans. This leads to the natural
Externí odkaz:
http://arxiv.org/abs/2405.15116
Many data analytics systems store and process large datasets in partitions containing millions of rows. By mapping rows to partitions in an optimized way, it is possible to improve query performance by skipping over large numbers of irrelevant partit
Externí odkaz:
http://arxiv.org/abs/2405.04984
We study the classic correlation clustering in the dynamic setting. Given $n$ objects and a complete labeling of the object-pairs as either similar or dissimilar, the goal is to partition the objects into arbitrarily many clusters while minimizing di
Externí odkaz:
http://arxiv.org/abs/2404.06797
In the kernel density estimation (KDE) problem one is given a kernel $K(x, y)$ and a dataset $P$ of points in a Euclidean space, and must prepare a data structure that can quickly answer density queries: given a point $q$, output a $(1+\epsilon)$-app
Externí odkaz:
http://arxiv.org/abs/2401.02562
Given an arbitrary set of high dimensional points in $\ell_1$, there are known negative results that preclude the possibility of always mapping them to a low dimensional $\ell_1$ space while preserving distances with small multiplicative distortion.
Externí odkaz:
http://arxiv.org/abs/2312.02435
Autor:
Charikar, Moses, Gao, Ruiquan
We study the Ultrametric Violation Distance problem introduced by Cohen-Addad, Fan, Lee, and Mesmay [FOCS, 2022]. Given pairwise distances $x\in \mathbb{R}_{>0}^{\binom{[n]}{2}}$ as input, the goal is to modify the minimum number of distances so as t
Externí odkaz:
http://arxiv.org/abs/2311.04533
Clustering is a fundamental problem in unsupervised machine learning with many applications in data analysis. Popular clustering algorithms such as Lloyd's algorithm and $k$-means++ can take $\Omega(ndk)$ time when clustering $n$ points in a $d$-dime
Externí odkaz:
http://arxiv.org/abs/2310.16752
We introduce a new class of objectives for optimal transport computations of datasets in high-dimensional Euclidean spaces. The new objectives are parametrized by $\rho \geq 1$, and provide a metric space $\mathcal{R}_{\rho}(\cdot, \cdot)$ for discre
Externí odkaz:
http://arxiv.org/abs/2307.10042