@article{KoldaBader2009, author = {Kolda, Tamara G. and Bader, Brett W.}, title = {Tensor Decompositions and Applications}, journal = {SIAM Review}, volume = {51}, number = {3}, pages = {455-500}, year = {2009}, doi = {10.1137/07070111X}, URL = { https://doi.org/10.1137/07070111X}, eprint ={https://doi.org/10.1137/07070111X}, abstract = { This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N-way array. Decompositions of higher-order tensors (i.e., N-way arrays with \$N \geq 3\$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors. } } @article{RegMatrixReg-ZhouLi2014, author = {Zhou, Hua and Li, Lexin}, title = {Regularized matrix regression}, journal = {Journal of the Royal Statistical Society. Series B (Statistical Methodology)}, volume = {76}, number = {2}, pages = {463--483}, year = {2014}, publisher = {[Royal Statistical Society, Wiley]} } @inproceedings{Nesterov1983, title = {A method of solving a convex programming problem with convergence rate $O(1/k^2)$}, author = {Nesterov, Yurii Evgen'evich}, booktitle = {Doklady Akademii Nauk}, volume = {269}, number = {3}, pages = {543--547}, year = {1983}, organization= {Russian Academy of Sciences} } @book{StatInf-CasellaBerger2002, title = {{Statistical Inference}}, author = {Casella, George and Berger, Roger L.}, isbn = {0-534-24312-6}, series = {Duxbury Advanced Series}, year = {2002}, edition = {2}, publisher = {Thomson Learning} } @book{MatrixDiffCalc-MagnusNeudecker1999, title = {Matrix Differential Calculus with Applications in Statistics and Econometrics (Revised Edition)}, author = {Magnus, Jan R. and Neudecker, Heinz}, year = {1999}, publisher = {John Wiley \& Sons Ltd}, isbn = {0-471-98632-1} } @article{SymMatandJacobians-MagnusNeudecker1986, title = {Symmetry, 0-1 Matrices and Jacobians: A Review}, author = {Magnus, Jan R. and Neudecker, Heinz}, ISSN = {02664666, 14694360}, URL = {http://www.jstor.org/stable/3532421}, journal = {Econometric Theory}, number = {2}, pages = {157--190}, publisher = {Cambridge University Press}, urldate = {2023-10-03}, volume = {2}, year = {1986} } @book{MatrixAlgebra-AbadirMagnus2005, title = {Matrix Algebra}, author = {Abadir, Karim M. and Magnus, Jan R.}, year = {2005}, publisher = {Cambridge University Press}, series = {Econometric Exercises}, collection = {Econometric Exercises}, place = {Cambridge}, doi = {10.1017/CBO9780511810800} } @article{TensorDecomp-HuLeeWang2022, author = {Hu, Jiaxin and Lee, Chanwoo and Wang, Miaoyan}, title = {Generalized Tensor Decomposition With Features on Multiple Modes}, journal = {Journal of Computational and Graphical Statistics}, volume = {31}, number = {1}, pages = {204-218}, year = {2022}, publisher = {Taylor \& Francis}, doi = {10.1080/10618600.2021.1978471}, } @article{CovarEstSparseKron-LengPan2018, author = {Leng, Chenlei and Pan, Guangming}, title = {{Covariance estimation via sparse Kronecker structures}}, volume = {24}, journal = {Bernoulli}, number = {4B}, publisher = {Bernoulli Society for Mathematical Statistics and Probability}, pages = {3833 -- 3863}, year = {2018}, doi = {10.3150/17-BEJ980} } @article{sdr-PfeifferKaplaBura2021, author = {Pfeiffer, Ruth and Kapla, Daniel and Bura, Efstathia}, title = {{Least squares and maximum likelihood estimation of sufficient reductions in regressions with matrix-valued predictors}}, volume = {11}, year = {2021}, journal = {International Journal of Data Science and Analytics}, doi = {10.1007/s41060-020-00228-y} } @article{sdr-BuraDuarteForzani2016, author = {Bura, Efstathia and Duarte, Sabrina and Forzani, Liliana}, title = {Sufficient Reductions in Regressions With Exponential Family Inverse Predictors}, journal = {Journal of the American Statistical Association}, volume = {111}, number = {515}, pages = {1313-1329}, year = {2016}, publisher = {Taylor \& Francis}, doi = {10.1080/01621459.2015.1093944} } @article{FisherLectures-Cook2007, author = {Cook, R. Dennis}, journal = {Statistical Science}, month = {02}, number = {1}, pages = {1--26}, publisher = {The Institute of Mathematical Statistics}, title = {{Fisher Lecture: Dimension Reduction in Regression}}, volume = {22}, year = {2007}, doi = {10.1214/088342306000000682} } @article{asymptoticMLE-BuraEtAl2018, author = {Bura, Efstathia and Duarte, Sabrina and Forzani, Liliana and E. Smucler and M. Sued}, title = {Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models}, journal = {Statistics}, volume = {52}, number = {5}, pages = {1005-1024}, year = {2018}, publisher = {Taylor \& Francis}, doi = {10.1080/02331888.2018.1467420}, } @article{tsir-DingCook2015, author = {Shanshan Ding and R. Dennis Cook}, title = {Tensor sliced inverse regression}, journal = {Journal of Multivariate Analysis}, volume = {133}, pages = {216-231}, year = {2015}, issn = {0047-259X}, doi = {10.1016/j.jmva.2014.08.015} } @article{lsir-PfeifferForzaniBura, author = {Pfeiffer, Ruth and Forzani, Liliana and Bura, Efstathia}, year = {2012}, month = {09}, pages = {2414-27}, title = {Sufficient dimension reduction for longitudinally measured predictors}, volume = {31}, journal = {Statistics in medicine}, doi = {10.1002/sim.4437} } @Inbook{ApproxKron-VanLoanPitsianis1993, author = {Van Loan, C. F. and Pitsianis, N.}, editor = {Moonen, Marc S. and Golub, Gene H. and De Moor, Bart L. R.}, title = {Approximation with Kronecker Products}, bookTitle = {Linear Algebra for Large Scale and Real-Time Applications}, year = {1993}, publisher = {Springer Netherlands}, address = {Dordrecht}, pages = {293--314}, isbn = {978-94-015-8196-7}, doi = {10.1007/978-94-015-8196-7_17} } @book{asymStats-van_der_Vaart1998, title = {Asymptotic Statistics}, author = {{van der Vaart}, A.W.}, series = {Asymptotic Statistics}, year = {1998}, publisher = {Cambridge University Press}, series = {Cambridge Series in Statistical and Probabilistic Mathematics}, isbn = {0-521-49603-9} } @book{measureTheory-Kusolitsch2011, title = {{M}a\ss{}- und {W}ahrscheinlichkeitstheorie}, subtitle = {{E}ine {E}inf{\"u}hrung}, author = {Kusolitsch, Norbert}, series = {Springer-Lehrbuch}, year = {2011}, publisher = {Springer Vienna}, isbn = {978-3-7091-0684-6}, doi = {10.1007/978-3-7091-0685-3} } @book{optimMatrixMani-AbsilEtAl2007, title = {{Optimization Algorithms on Matrix Manifolds}}, author = {Absil, P.-A. and Mahony, R. and Sepulchre, R.}, year = {2007}, publisher = {Princeton University Press}, isbn = {9780691132983}, note = {Full Online Text \url{https://press.princeton.edu/absil}} } @Inbook{geomMethodsOnLowRankMat-Uschmajew2020, author = {Uschmajew, Andr{\'e} and Vandereycken, Bart}, editor = {Grohs, Philipp and Holler, Martin and Weinmann, Andreas}, title = {Geometric Methods on Low-Rank Matrix and Tensor Manifolds}, bookTitle = {Handbook of Variational Methods for Nonlinear Geometric Data}, year = {2020}, publisher = {Springer International Publishing}, address = {Cham}, pages = {261--313}, isbn = {978-3-030-31351-7}, doi = {10.1007/978-3-030-31351-7_9} } @book{introToSmoothMani-Lee2012, title = {Introduction to Smooth Manifolds}, author = {Lee, John M.}, year = {2012}, journal = {Graduate Texts in Mathematics}, publisher = {Springer New York}, doi = {10.1007/978-1-4419-9982-5} } @book{introToRiemannianMani-Lee2018, title = {Introduction to Riemannian Manifolds}, author = {Lee, John M.}, year = {2018}, journal = {Graduate Texts in Mathematics}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-319-91755-9} } @misc{MLEonManifolds-HajriEtAl2017, title = {Maximum Likelihood Estimators on Manifolds}, author = {Hajri, Hatem and Said, Salem and Berthoumieu, Yannick}, year = {2017}, journal = {Lecture Notes in Computer Science}, publisher = {Springer International Publishing}, pages = {692-700}, doi = {10.1007/978-3-319-68445-1_80} } @article{relativity-Einstain1916, author = {Einstein, Albert}, title = {Die Grundlage der allgemeinen Relativitätstheorie}, year = {1916}, journal = {Annalen der Physik}, volume = {354}, number = {7}, pages = {769-822}, doi = {10.1002/andp.19163540702} } @article{MultilinearOperators-Kolda2006, title = {Multilinear operators for higher-order decompositions.}, author = {Kolda, Tamara Gibson}, doi = {10.2172/923081}, url = {https://www.osti.gov/biblio/923081}, place = {United States}, year = {2006}, month = {4}, type = {Technical Report} } @book{aufbauAnalysis-kaltenbaeck2021, title = {Aufbau Analysis}, author = {Kaltenb\"ack, Michael}, isbn = {978-3-88538-127-3}, series = {Berliner Studienreihe zur Mathematik}, edition = {27}, year = {2021}, publisher = {Heldermann Verlag} } @article{TensorNormalMLE-ManceurDutilleul2013, title = {Maximum likelihood estimation for the tensor normal distribution: Algorithm, minimum sample size, and empirical bias and dispersion}, author = {Ameur M. Manceur and Pierre Dutilleul}, journal = {Journal of Computational and Applied Mathematics}, volume = {239}, pages = {37-49}, year = {2013}, issn = {0377-0427}, doi = {10.1016/j.cam.2012.09.017}, url = {https://www.sciencedirect.com/science/article/pii/S0377042712003810} } @article{StatIdentTensorGaussian-DeesMandic2019, title = {A Statistically Identifiable Model for Tensor-Valued Gaussian Random Variables}, author = {Bruno Scalzo Dees and Danilo P. Mandic}, journal = {ArXiv}, year = {2019}, volume = {abs/1911.02915}, url = {https://api.semanticscholar.org/CorpusID:207847615} } @article{Ising-Ising1924, author = {Ising, Ernst}, title = {{Beitrag zur Theorie des Ferromagnetismus}}, journal = {Zeitschrift f\"ur Physik}, pages = {253-258}, volume = {31}, number = {1}, year = {1924}, month = {2}, issn = {0044-3328}, doi = {10.1007/BF02980577} } # TODO: Fix the following!!! @book{GraphicalModels-Whittaker2009, author = {J. Whittaker}, title = {Graphical Models in Applied Multivariate Statistics}, publisher = {Wiley}, year = {2009} } @article{MVB-Dai2012, author = {B. Dai}, title = {Multivariate bernoulli distribution models}, year = {2012} } @article{MVB-DaiDingWahba2013, author = {B. Dai, S. Ding, and G. Wahba}, title = {Multivariate bernoulli distribution}, year = {2013} } @article{sdr-mixedPredictors-BuraForzaniEtAl2022, author = {Bura and Forzani and TODO}, title = {Sufficient reductions in regression with mixed predictors}, journal = {}, volume = {}, number = {}, year = {2022} } @article{sparseIsing-ChengEtAt2014, author = {J. Cheng, E. Levina, and J. Wang, P.and Zhu}, title = {A sparse Ising model with covariates}, journal = {}, volume = {}, number = {}, year = {2014}, doi = {10.1111/biom.12202} } @book{deeplearningbook-GoodfellowEtAl2016, title = {Deep Learning}, author = {Ian Goodfellow and Yoshua Bengio and Aaron Courville}, publisher = {MIT Press}, url = {\url{http://www.deeplearningbook.org}}, year = {2016} } @misc{rmsprop-Hinton2012, title = {Neural networks for machine learning}, author = {Hinton, G.}, year = {2012} } @article{self-kapla2019, title = {Comparison of Different Word Embeddings and Neural Network Types for Sentiment Analysis of German Political Speeches}, author = {Kapla, Daniel}, year = {2019} } @article{MGCCA-GirkaEtAl2024, title = {Tensor generalized canonical correlation analysis}, author = {Fabien Girka and Arnaud Gloaguen and Laurent {Le Brusquet} and Violetta Zujovic and Arthur Tenenhaus}, year = {2024}, journal = {Information Fusion}, volume = {102}, issn = {1566-2535}, doi = {10.1016/j.inffus.2023.102045} } @Article{Rdimtools, title = {{Rdimtools}: An {R} Package for Dimension Reduction and Intrinsic Dimension Estimation}, author = {Kisung You and Dennis Shung}, journal = {Software Impacts}, year = {2022}, volume = {14}, issn = {26659638}, pages = {100414}, doi = {10.1016/j.simpa.2022.100414}, } @misc{lichess-database, author = {{Thibault Duplessis}}, title = {lichess.org open database}, year = {2013}, url = {https://database.lichess.org}, note = {visited on December 8, 2023}, } @misc{stockfish, title = {Stockfish}, year = {since 2008}, author = {{The Stockfish developers (see \href{https://github.com/official-stockfish/Stockfish/blob/master/AUTHORS}{AUTHORS} file)}}, url = {https://stockfishchess.org/}, abstract = {Stockfish is a free and strong UCI chess engine.}, } @mish{schachhoernchen, title = {Schach H\"ornchen}, year = {development since 2021, first release pending}, author = {Kapla, Daniel}, url = {todo!} }