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pro vyhledávání: '"Compton, Spencer"'
Autor:
Sokota, Samuel, Sam, Dylan, de Witt, Christian Schroeder, Compton, Spencer, Foerster, Jakob, Kolter, J. Zico
Minimum-entropy coupling (MEC) -- the process of finding a joint distribution with minimum entropy for given marginals -- has applications in areas such as causality and steganography. However, existing algorithms are either computationally intractab
Externí odkaz:
http://arxiv.org/abs/2405.19540
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:
Compton, Spencer, Valiant, Gregory
Given data drawn from a collection of Gaussian variables with a common mean but different and unknown variances, what is the best algorithm for estimating their common mean? We present an intuitive and efficient algorithm for this task. As different
Externí odkaz:
http://arxiv.org/abs/2312.02417
Autor:
Compton, Spencer
It is a task of widespread interest to learn the underlying causal structure for systems of random variables. Entropic Causal Inference is a recent framework for learning the causal graph between two variables from observational data (i.e., without e
Externí odkaz:
https://hdl.handle.net/1721.1/145148
Given a set of discrete probability distributions, the minimum entropy coupling is the minimum entropy joint distribution that has the input distributions as its marginals. This has immediate relevance to tasks such as entropic causal inference for c
Externí odkaz:
http://arxiv.org/abs/2302.11838
Autor:
Compton, Spencer
We examine the minimum entropy coupling problem, where one must find the minimum entropy variable that has a given set of distributions $S = \{p_1, \dots, p_m \}$ as its marginals. Although this problem is NP-Hard, previous works have proposed algori
Externí odkaz:
http://arxiv.org/abs/2203.05108
Entropic causal inference is a framework for inferring the causal direction between two categorical variables from observational data. The central assumption is that the amount of unobserved randomness in the system is not too large. This unobserved
Externí odkaz:
http://arxiv.org/abs/2101.03501
Interval scheduling is a basic problem in the theory of algorithms and a classical task in combinatorial optimization. We develop a set of techniques for partitioning and grouping jobs based on their starting and ending times, that enable us to view
Externí odkaz:
http://arxiv.org/abs/2012.15002
Autor:
Bosboom, Jeffrey, Chen, Charlotte, Chung, Lily, Compton, Spencer, Coulombe, Michael, Demaine, Erik D., Demaine, Martin L., Filho, Ivan Tadeu Ferreira Antunes, Hendrickson, Dylan, Hesterberg, Adam, Hsu, Calvin, Hu, William, Korten, Oliver, Luo, Zhezheng, Zhang, Lillian
We analyze the computational complexity of several new variants of edge-matching puzzles. First we analyze inequality (instead of equality) constraints between adjacent tiles, proving the problem NP-complete for strict inequalities but polynomial for
Externí odkaz:
http://arxiv.org/abs/2002.03887
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