Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Gayen, Sutanu"'
Bayes nets are extensively used in practice to efficiently represent joint probability distributions over a set of random variables and capture dependency relations. In a seminal paper, Chickering et al. (JMLR 2004) showed that given a distribution $
Externí odkaz:
http://arxiv.org/abs/2407.00927
Autor:
Bhattacharyya, Arnab, Gayen, Sutanu, John, Philips George, Sen, Sayantan, Vinodchandran, N. V.
This work establishes a novel link between the problem of PAC-learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, using an online learning framework. We observe that if we apply the expone
Externí odkaz:
http://arxiv.org/abs/2405.07914
Autor:
Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S., Myrisiotis, Dimitrios, Pavan, A., Vinodchandran, N. V.
We show that computing the total variation distance between two product distributions is $\#\mathsf{P}$-complete. This is in stark contrast with other distance measures such as Kullback-Leibler, Chi-square, and Hellinger, which tensorize over the mar
Externí odkaz:
http://arxiv.org/abs/2405.08255
Autor:
Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S., Myrisiotis, Dimitrios, Pavan, A., Vinodchandran, N. V.
In this paper, we establish a novel connection between total variation (TV) distance estimation and probabilistic inference. In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabil
Externí odkaz:
http://arxiv.org/abs/2309.09134
Autor:
Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S., Myrisiotis, Dimitrios, Pavan, A., Vinodchandran, N. V.
Publikováno v:
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (2023) Main Track. Pages 3479-3487
Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the problem of computing the TV distance of two product distributions over the domain $\{0,1\}^n$. In p
Externí odkaz:
http://arxiv.org/abs/2206.07209
Autor:
Bhattacharyya, Arnab, Gayen, Sutanu, Kandasamy, Saravanan, Raval, Vedant, Vinodchandran, N. V.
We consider the problem of efficiently inferring interventional distributions in a causal Bayesian network from a finite number of observations. Let $\mathcal{P}$ be a causal model on a set $\mathbf{V}$ of observable variables on a given causal graph
Externí odkaz:
http://arxiv.org/abs/2107.11712
Gaussian Bayesian networks (a.k.a. linear Gaussian structural equation models) are widely used to model causal interactions among continuous variables. In this work, we study the problem of learning a fixed-structure Gaussian Bayesian network up to a
Externí odkaz:
http://arxiv.org/abs/2107.10450
We study the problems of identity and closeness testing of $n$-dimensional product distributions. Prior works by Canonne, Diakonikolas, Kane and Stewart (COLT 2017) and Daskalakis and Pan (COLT 2017) have established tight sample complexity bounds fo
Externí odkaz:
http://arxiv.org/abs/2012.14632
We provide finite sample guarantees for the classical Chow-Liu algorithm (IEEE Trans.~Inform.~Theory, 1968) to learn a tree-structured graphical model of a distribution. For a distribution $P$ on $\Sigma^n$ and a tree $T$ on $n$ nodes, we say $T$ is
Externí odkaz:
http://arxiv.org/abs/2011.04144
We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions. Specifically, we show algorithms for the following problems: - Given sample access to two Bayesian networks $P_1$ and $P_2$ over k
Externí odkaz:
http://arxiv.org/abs/2002.05378