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Measuring the dependence of data plays a central role in statistics and machine learning.
Conditional association
Seth, S.; and Príncipe, J. C. 2012 · 1905
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On measures of dependence
Rényi, A. 1959 · 1959
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Information theoretical analysis of multivariate correlation
Watanabe, S. 1960 · 1960
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Linear dependence structure of the entropy space
Sun, T. 1975 · 1975
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Linear prediction, filtering, and smoothing: An information-theoretic approach
Kalata, P.; and Priemer, R. 1979 · 1979
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On differentiating eigenvalues and eigenvectors
Magnus, J. R. 1985 · 1985
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Some intersection theorems for ordered sets and graphs
Chung, F. R.; Graham, R. L.; Frankl, P.; and Shearer, J. B. 1986 · 1986
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Relative entropy measures of multivariate dependence
Joe, H. 1989 · 1989
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Optimal state estimation for stochastic systems: An information theoretic approach
Feng, X.; Loparo, K. A.; and Fang, Y. 1997 · 1997
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When is “nearest neighbor” meaningful?
Beyer, K.; Goldstein, J.; Ramakrishnan, R.; and Shaft, U. 1999 · 1999
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Elements of information theory
Cover, T. M. 1999 · 1999
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The information bottleneck method
Tishby, N.; Pereira, F. C.; and Bialek, W. 1999 · 1999
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LOF: identifying density-based local outliers
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; and Sander, J. 2000 · 2000
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Learning from examples with information theoretic criteria
Principe, J. C.; Xu, D.; Zhao, Q.; and Fisher, J. W. 2000 · 2000
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Kernel independent component analysis
Bach, F. R.; and Jordan, M. I. 2002 · 2002
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An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems
Erdogmus, D.; and Principe, J. C. 2002 · 2002
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Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Bay, S. D.; and Schwabacher, M. 2003 · 2003
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Information theory, inference and learning algorithms
MacKay, D. J. 2003 · 2003
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Estimating mutual information
Kraskov, A.; Stögbauer, H.; and Grassberger, P. 2004 · 2004
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Cumulative residual entropy: a new measure of information
Rao, M.; Chen, Y.; Vemuri, B. C.; and Wang, F. 2004 · 2004
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Measuring statistical dependence with Hilbert-Schmidt norms
Gretton, A.; Bousquet, O.; Smola, A.; and Schölkopf, B. 2005 · 2005
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Feature bagging for outlier detection
Lazarevic, A.; and Kumar, V. 2005 · 2005
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Infinitely divisible matrices
Bhatia, R. 2006 · 2006
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Statistical comparisons of classifiers over multiple data sets
Demšar, J. 2006 · 2006
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Multivariate extensions of Spearman’s rho and related statistics
Schmid, F.; and Schmidt, R. 2007 · 2007
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Measuring and testing dependence by correlation of distances
Székely, G. J.; Rizzo, M. L.; Bakirov, N. K.; et al. 2007 · 2007
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A kernel statistical test of independence
Gretton, A.; Fukumizu, K.; Teo, C. H.; Song, L.; Schölkopf, B.; and Smola, A. J. 2008 · 2008
Cited alongside, same era.
Angle-based outlier detection in high-dimensional data
Kriegel, H.-P.; Schubert, M.; and Zimek, A. 2008 · 2008
Cited alongside, same era.
Direct importance estimation for covariate shift adaptation
Sugiyama, M.; Suzuki, T.; Nakajima, S.; Kashima, H.; von Bünau, P.; and Kawanabe, M. 2008 · 2008
Cited alongside, same era.
Information theoretic interpretation of error criteria
Chen, B.; Hu, J.-C.; Zhu, Y.; and Sun, Z.-Q. 2009 · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, A. 2009 · 2009
Cited alongside, same era.
Measures of entropy from data using infinitely divisible kernels
Sanchez Giraldo, L. G.; Rao, M.; and Principe, J. C. 2014 · 2014
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Non-parametric entropy estimation toolbox (npeet)
Ver Steeg, G. 2014 · 2014
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Tensorflow: A system for large-scale machine learning
Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; et al. 2016 · 2016
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Measuring multivariate association and beyond
Josse, J.; and Holmes, S. 2016 · 2016
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ODDS Library
Rayana, S. 2016 · 2016
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Measuring dependency via intrinsic dimensionality
Romano, S.; Chelly, O.; Nguyen, V.; Bailey, J.; and Houle, M. E. 2016 · 2016
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Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data
Müller, E.; Assent, I.; Günnemann, S.; Krieger, R.; and Seidl, T. 2009 · 2009
Cited alongside, same era.
Dataset shift in machine learning
Quionero-Candela, J.; Sugiyama, M.; Schwaighofer, A.; and Lawrence, N. D. 2009 · 2009
Cited alongside, same era.
A new interpretation on the MMSE as a robust MEE criterion
Chen, B.; Zhu, Y.; Hu, J.; and Zhang, M. 2010 · 2010
Cited alongside, same era.
Mutual information is critically dependent on prior assumptions: would the correct estimate of mutual information please identify itself?
Fernandes, A. D.; and Gloor, G. B. 2010 · 2010
Cited alongside, same era.
Information inequalities for joint distributions, with interpretations and applications
Madiman, M.; and Tetali, P. 2010 · 2010
Cited alongside, same era.
Detecting novel associations in large data sets
Reshef, D. N.; Reshef, Y. A.; Finucane, H. K.; Grossman, S. R.; McVean, G.; Turnbaugh, P. J.; Lander, E. S.; Mitzenmacher, M.; and Sabeti, P. C. 2011 · 2011
Cited alongside, same era.
Deep variational information bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2017 · 2017
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Regularizing neural networks by penalizing confident output distributions
Pereyra, G.; Tucker, G.; Chorowski, J.; Kaiser, Ł.; and Hinton, G. 2017 · 2017
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Raghu, M.; Gilmer, J.; Yosinski, J.; and Sohl-Dickstein, J. 2017 · 2017
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Opening the black box of deep neural networks via information
Shwartz-Ziv, R.; and Tishby, N. 2017 · 2017
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Unbiased multivariate correlation analysis
Wang, Y.; Romano, S.; Nguyen, V.; Bailey, J.; Ma, X.; and Xia, S.-T. 2017 · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
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Multi-variate correlation and mixtures of product measures
Austin, T. 2018 · 2018
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Mutual information neural estimation
Belghazi, M. I.; Baratin, A.; Rajeshwar, S.; Ozair, S.; Bengio, Y.; Courville, A.; and Hjelm, D. 2018 · 2018
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On the Information Bottleneck Theory of Deep Learning
Saxe, A. M.; Bansal, Y.; Dapello, J.; Advani, M.; Kolchinsky, A.; Tracey, B. D.; and Cox, D. D. 2018 · 2018
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Large-scale kernel methods for independence testing
Zhang, Q.; Filippi, S.; Gretton, A.; and Sejdinovic, D. 2018 · 2018
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Learning representations for neural network-based classification using the information bottleneck principle
Amjad, R. A.; and Geiger, B. C. 2019 · 2019
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Nonlinear information bottleneck
Kolchinsky, A.; Tracey, B. D.; and Wolpert, D. H. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019 · 2019
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Preventing failures due to dataset shift: Learning predictive models that transport
Subbaswamy, A.; Schulam, P.; and Saria, S. 2019 · 2019
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Fast Approximation of Empirical Entropy via Subsampling
Wang, C.; and Ding, B. 2019 · 2019
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Multivariate Extension of Matrix-based Renyi’s α \alpha -order Entropy Functional
Yu, S.; Sanchez Giraldo, L. G.; Jenssen, R.; and Principe, J. C. 2019 · 2019
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Robust learning with the Hilbert-Schmidt independence criterion
Greenfeld, D.; and Shalit, U. 2020 · 2020
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A survey of unsupervised deep domain adaptation
Wilson, G.; and Cook, D. J. 2020 · 2020
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Understanding convolutional neural networks with information theory: An initial exploration
Yu, S.; Wickstrøm, K.; Jenssen, R.; and Principe, J. C. 2020 · 2020
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