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Conditional Mutual Information (CMI) is a measure of conditional dependence between random variables X and Y, given another random variable Z.
Sample estimate of the entropy of a random vector
LF Kozachenko and Nikolai N Leonenko · 1987
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Nonparametric entropy estimation: An overview
Jan Beirlant, Edward J Dudewicz, László Györfi, and Edward C Van der Meulen · 1997
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Entropy and inference, revisited
Ilya Nemenman, Fariel Shafee, and William Bialek · 2002
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A new class of entropy estimators for multi-dimensional densities
Erik G Miller · 2003
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Nearest neighbor estimates of entropy
Harshinder Singh, Neeraj Misra, Vladimir Hnizdo, Adam Fedorowicz, and Eugene Demchuk · 2003
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Fast binary feature selection with conditional mutual information
François Fleuret · 2004
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Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger · 2004
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Obtaining calibrated probabilities from boosting
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Causal protein-signaling networks derived from multiparameter single-cell data
Karen Sachs, Omar Perez, Dana Pe’er, Douglas A Lauffenburger, and Garry P Nolan · 2005
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Learning module networks
Eran Segal, Dana Pe’er, Aviv Regev, Daphne Koller, and Nir Friedman · 2005
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Partial mutual information for coupling analysis of multivariate time series
Stefan Frenzel and Bernd Pompe · 2007
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Gene regulatory network reconstruction using conditional mutual information
Kuo-Ching Liang and Xiaodong Wang · 2008
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Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan · 2008
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Approximating mutual information by maximum likelihood density ratio estimation
Taiji Suzuki, Masashi Sugiyama, Jun Sese, and Takafumi Kanamori · 2008
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Inferring the directionality of coupling with conditional mutual information
Martin Vejmelka and Milan Paluš · 2008
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Sample-spacings-based density and entropy estimators for spherically invariant multidimensional data
Intae Lee · 2010
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Nonrigid image registration using conditional mutual information
Dirk Loeckx, Pieter Slagmolen, Frederik Maes, Dirk Vandermeulen, and Paul Suetens · 2010
Cited alongside, same era.
Estimation of rényi entropy and mutual information based on generalized nearest-neighbor graphs
Dávid Pál, Barnabás Póczos, and Csaba Szepesvári · 2010
Cited alongside, same era.
Characterization of the causality between spike trains with permutation conditional mutual information
Zhaohui Li, Gaoxiang Ouyang, Duan Li, and Xiaoli Li · 2011
Cited alongside, same era.
Estimating a causal order among groups of variables in linear models
Doris Entner and Patrik O Hoyer · 2012
Cited alongside, same era.
Estimation of nonlinear functionals of densities with confidence
Kumar Sricharan, Raviv Raich, and Alfred O Hero · 2012
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Reliability of inference of directed climate networks using conditional mutual information
Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Breaking the bandwidth barrier: Geometrical adaptive entropy estimation
Weihao Gao, Sewoong Oh, and Pramod Viswanath · 2016
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Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2016
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Bayesian networks for variable groups
Pekka Parviainen and Samuel Kaski · 2016
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Finite-sample analysis of fixed-k nearest neighbor density functional estimators
Shashank Singh and Barnabás Póczos · 2016
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Estimating mutual information for discrete-continuous mixtures
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Jaroslav Hlinka, David Hartman, Martin Vejmelka, Jakob Runge, Norbert Marwan, Jürgen Kurths, and Milan Paluš · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Cited alongside, same era.
Ensemble estimators for multivariate entropy estimation
Kumar Sricharan, Dennis Wei, and Alfred O Hero · 2013
Cited alongside, same era.
A permutation-based kernel conditional independence test
G Doran, K Muandet, K Zhang, and B Schölkopf · 2014
Cited alongside, same era.
Inferring protein modulation from gene expression data using conditional mutual information
Federico M Giorgi, Gonzalo Lopez, Jung H Woo, Brygida Bisikirska, Andrea Califano, and Mukesh Bansal · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Expected entropy as a measure and criterion of randomness of binary sequences
Marek Leśniewicz · 2014
Cited alongside, same era.
Weihao Gao, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Model-powered conditional independence test
Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G Dimakis, and Sanjay Shakkottai · 2017
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Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
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Demystifying fixed k k -nearest neighbor information estimators
Weihao Gao, Sewoong Oh, and Pramod Viswanath · 2018
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The nearest neighbor information estimator is adaptively near minimax rate-optimal
Jiantao Jiao, Weihao Gao, and Yanjun Han · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Estimators for multivariate information measures in general probability spaces
Arman Rahimzamani, Himanshu Asnani, Pramod Viswanath, and Sreeram Kannan · 2018
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Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Jakob Runge · 2018
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Mimic and classify: A meta-algorithm for conditional independence testing
Rajat Sen, Karthikeyan Shanmugam, Himanshu Asnani, Arman Rahimzamani, and Sreeram Kannan · 2018
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