literature.bib

@article{gaussian_bounds,
  issn      = {00034851},
  url       = {http://www.jstor.org/stable/2235868},
  author    = {Robert D. Gordon},
  journal   = {The Annals of Mathematical Statistics},
  number    = {3},
  pages     = {364--366},
  publisher = {Institute of Mathematical Statistics},
  title     = {Values of Mills' Ratio of Area to Bounding Ordinate and of the Normal Probability Integral for Large Values of the Argument},
  volume    = {12},
  year      = {1941}
}

@inproceedings{feldman-langberg-coresets,
  author    = {Feldman, Dan and Langberg, Michael},
  title     = {A Unified Framework for Approximating and Clustering Data},
  year      = {2011},
  isbn      = {9781450306911},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/1993636.1993712},
  doi       = {10.1145/1993636.1993712},
  booktitle = {Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing},
  pages     = {569–578},
  numpages  = {10},
  keywords  = {regression, clustering, pca, pac-learning, epsilon-approximation, svd, k-means, k-median, coresets, epsilon-nets, approximating, cur},
  location  = {San Jose, California, USA},
  series    = {STOC '11}
}

@article{braverman-feldman-coresets,
  author     = {Vladimir Braverman and
                Dan Feldman and
                Harry Lang},
  title      = {New Frameworks for Offline and Streaming Coreset Constructions},
  journal    = {CoRR},
  volume     = {abs/1612.00889},
  year       = {2016},
  url        = {http://arxiv.org/abs/1612.00889},
  eprinttype = {arXiv},
  eprint     = {1612.00889},
  timestamp  = {Mon, 13 Aug 2018 16:47:41 +0200},
  biburl     = {https://dblp.org/rec/journals/corr/BravermanFL16.bib},
  bibsource  = {dblp computer science bibliography, https://dblp.org}
}

@article{big-data-tiny-data,
  author  = {Feldman, Dan and Schmidt, Melanie and Sohler, Christian},
  title   = {Turning Big Data Into Tiny Data: Constant-Size Coresets for  $k$-Means, PCA, and Projective Clustering},
  journal = {SIAM Journal on Computing},
  volume  = {49},
  number  = {3},
  pages   = {601-657},
  year    = {2020},
  doi     = {10.1137/18M1209854},
  url     = {https://doi.org/10.1137/18M1209854},
  eprint  = {https://doi.org/10.1137/18M1209854}
}

@inproceedings{langberg-schulman-sensitivities,
  author    = {Langberg, Michael and Schulman, Leonard J.},
  title     = {Universal $\epsilon$-Approximators for Integrals},
  year      = {2010},
  isbn      = {9780898716986},
  booktitle = {Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms},
  pages     = {598-607},
  numpages  = {10},
  location  = {Austin, Texas},
  series    = {SODA '10}
}

@inproceedings{on-coresets,
  author     = {Alexander Munteanu and
                Chris Schwiegelshohn and
                Christian Sohler and
                David P. Woodruff},
  _editor    = {Samy Bengio and
                Hanna M. Wallach and
                Hugo Larochelle and
                Kristen Grauman and
                Nicol{\`{o}} Cesa{-}Bianchi and
                Roman Garnett},
  title      = {On Coresets for Logistic Regression},
  _booktitle = {Advances in Neural Information Processing Systems 31: Annual Conference
                on Neural Information Processing Systems 2018, NeurIPS 2018, December
                3-8, 2018, Montr{\'{e}}al, Canada},
  booktitle  = {Advances in Neural Information Processing Systems 31, {(NeurIPS)}},
  pages      = {6562--6571},
  year       = {2018},
  _url       = {https://proceedings.neurips.cc/paper/2018/hash/63bfd6e8f26d1d3537f4c5038264ef36-Abstract.html},
  _timestamp = {Thu, 21 Jan 2021 15:15:20 +0100},
  _biburl    = {https://dblp.org/rec/conf/nips/MunteanuSSW18.bib},
  _bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{munteanu-coresets-introduction,
  author  = {Munteanu, Alexander
             and Schwiegelshohn, Chris},
  title   = {Coresets-Methods and History: A Theoreticians Design Pattern for Approximation and Streaming Algorithms},
  journal = {KI - K{\"u}nstliche Intelligenz},
  year    = {2018},
  month   = {Feb},
  day     = {01},
  volume  = {32},
  number  = {1},
  pages   = {37-53},
  issn    = {1610-1987},
  doi     = {10.1007/s13218-017-0519-3},
  url     = {https://doi.org/10.1007/s13218-017-0519-3}
}

@article{index,
  title     = {On randomized one-round communication complexity},
  author    = {Kremer, Ilan and Nisan, Noam and Ron, Dana},
  journal   = {Computational Complexity},
  volume    = {8},
  number    = {1},
  pages     = {21--49},
  year      = {1999},
  publisher = {Springer}
}

@book{matrix-computations,
  author    = {Golub, Gene H. and van Loan, Charles F.},
  edition   = {Fourth},
  isbn      = {1421407949 9781421407944},
  publisher = {JHU Press},
  title     = {Matrix Computations},
  year      = 2013
}

@article{online-row-sampling,
  author    = {Cohen, Michael B. and Musco, Cameron and Pachocki, Jakub},
  title     = {Online Row Sampling},
  year      = {2020},
  pages     = {1--25},
  doi       = {10.4086/toc.2020.v016a015},
  publisher = {Theory of Computing},
  journal   = {Theory of Computing},
  volume    = {16},
  number    = {15},
  url       = {http://www.theoryofcomputing.org/articles/v016a015}
}

@book{computational-learning-theory,
  author    = {Michael J. Kearns and
               Umesh V. Vazirani},
  title     = {An Introduction to Computational Learning Theory},
  publisher = {{MIT} Press},
  year      = {1994},
  url       = {https://mitpress.mit.edu/books/introduction-computational-learning-theory},
  isbn      = {978-0-262-11193-5}
}

@article{tensor-factorization,
  author        = {Rachit Chhaya and
                   Jayesh Choudhari and
                   Anirban Dasgupta and
                   Supratim Shit},
  title         = {Streaming Coresets for Symmetric Tensor Factorization},
  journal       = {CoRR},
  volume        = {abs/2006.01225},
  year          = {2020},
  url           = {https://arxiv.org/abs/2006.01225},
  archiveprefix = {arXiv},
  eprint        = {2006.01225}
}

@article{vc-dimension-partition,
  title   = {Results on learnability and the Vapnik-Chervonenkis dimension},
  journal = {Information and Computation},
  volume  = {90},
  number  = {1},
  pages   = {33-49},
  year    = {1991},
  issn    = {0890-5401},
  doi     = {https://doi.org/10.1016/0890-5401(91)90058-A},
  url     = {https://www.sciencedirect.com/science/article/pii/089054019190058A},
  author  = {Nathan Linial and Yishay Mansour and Ronald L. Rivest}
}

@article{probit-computational,
  title   = {Computational aspects of probit model},
  author  = {E. Demidenko},
  journal = {Mathematical Communications},
  year    = {2001},
  volume  = {6},
  pages   = {233-247}
}

@article{probit-existence,
  title   = {Existence and Uniqueness of the Maximum Likelihood Estimator for a Multivariate Probit Model},
  author  = {E. Lesaffre and H. Kaufmann},
  journal = {Journal of the American Statistical Association},
  year    = {1992},
  volume  = {87},
  pages   = {805-811}
}

@book{regression-fahrmeir,
  author    = {Ludwig Fahrmeir and Thomas Kneib and Stefan Lang and Brian D. Marx},
  title     = {Regression},
  subtitle  = {Models, Methods and Applications},
  publisher = {Springer-Verlag Berlin Heidelberg},
  year      = {2013}
}

@book{glm-nelder,
  author    = {P. McCullagh and John A. Nelder},
  title     = {Generalized Linear Models},
  publisher = {Chapman and Hall/CRC},
  year      = {1989}
}

@book{glm-agresti,
  author    = {Alan Agresti},
  title     = {Foundations of Linear and Generalized Linear Models},
  publisher = {Wiley},
  year      = {2015}
}

@article{wedderburn,
  author  = {Wedderburn, R. W. M.},
  title   = {{On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models}},
  journal = {Biometrika},
  volume  = {63},
  number  = {1},
  pages   = {27-32},
  year    = {1976},
  month   = {04},
  issn    = {0006-3444}
}

@book{numerical-optimization,
  author    = {Jorge Nocedal and S. Wright},
  title     = {Numerical Optimization},
  publisher = {Springer-Verlag New York},
  year      = {2006},
  edition   = {2}
}

@book{bayes-gelman,
  author    = {Andrew Gelman and John Carlin and Hal Stern and David Dunson and Aki Vehtari and Donald Rubin},
  title     = {Bayesian Data Analysis},
  publisher = {Chapman and Hall/CRC},
  year      = {2013},
  edition   = {3}
}

@article{gibbs-probit-albert-chib,
  author    = {James H. Albert and Siddhartha Chib},
  title     = {Bayesian Analysis of Binary and Polychotomous Response Data},
  journal   = {Journal of the American Statistical Association},
  volume    = {88},
  number    = {422},
  pages     = {669-679},
  year      = {1993},
  publisher = {Taylor & Francis}
}

@article{gibbs-sampler,
  author    = {Alan E. Gelfand and Adrian F. M. Smith},
  journal   = {Journal of the American Statistical Association},
  number    = {410},
  pages     = {398--409},
  publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
  title     = {Sampling-Based Approaches to Calculating Marginal Densities},
  volume    = {85},
  year      = {1990}
}

@article{data-augmentation,
  author    = {Martin A. Tanner and Wing Hung Wong},
  journal   = {Journal of the American Statistical Association},
  number    = {398},
  pages     = {528--540},
  publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
  title     = {The Calculation of Posterior Distributions by Data Augmentation},
  volume    = {82},
  year      = {1987}
}

@book{glivenko-cantelli,
  place      = {Cambridge},
  series     = {Cambridge Series in Statistical and Probabilistic Mathematics},
  title      = {Asymptotic Statistics},
  publisher  = {Cambridge University Press},
  author     = {Vaart, A. W. van der},
  year       = {1998},
  collection = {Cambridge Series in Statistical and Probabilistic Mathematics}
}
@inproceedings{scalable-bayesian-logreg,
  author    = {Huggins, Jonathan H. and Campbell, Trevor and Broderick, Tamara},
  title     = {Coresets for Scalable Bayesian Logistic Regression},
  year      = {2016},
  isbn      = {9781510838819},
  publisher = {Curran Associates Inc.},
  address   = {Red Hook, NY, USA},
  booktitle = {Proceedings of the 30th International Conference on Neural Information Processing Systems},
  pages     = {4087–4095},
  numpages  = {9},
  location  = {Barcelona, Spain},
  series    = {NIPS'16}
}

@article{leverage-scores-drineas,
  author  = {Petros Drineas and Malik Magdon-Ismail and Michael W. Mahoney and David P. Woodruff},
  title   = {Fast Approximation of Matrix Coherence and Statistical Leverage},
  journal = {Journal of Machine Learning Research},
  year    = {2012},
  volume  = {13},
  number  = {111},
  pages   = {3475-3506},
  url     = {http://jmlr.org/papers/v13/drineas12a.html}
}

@article{reservoir-sampler,
  issn      = {00063444},
  url       = {http://www.jstor.org/stable/2336002},
  author    = {M. T. Chao},
  journal   = {Biometrika},
  number    = {3},
  pages     = {653--656},
  publisher = {[Oxford University Press, Biometrika Trust]},
  title     = {A General Purpose Unequal Probability Sampling Plan},
  volume    = {69},
  year      = {1982}
}

@article{woodruff-2017,
  author     = {Clarkson, Kenneth L. and Woodruff, David P.},
  title      = {Low-Rank Approximation and Regression in Input Sparsity Time},
  year       = {2017},
  issue_date = {February 2017},
  publisher  = {Association for Computing Machinery},
  address    = {New York, NY, USA},
  volume     = {63},
  number     = {6},
  issn       = {0004-5411},
  url        = {https://doi.org/10.1145/3019134},
  doi        = {10.1145/3019134},
  journal    = {J. ACM},
  month      = jan,
  articleno  = {54},
  numpages   = {45}
}

@article{johnson-lindenstrauss,
  title   = {Extensions of {L}ipschitz mappings into a {H}ilbert space},
  author  = {Johnson, William B and Lindenstrauss, Joram},
  journal = {Contemporary mathematics},
  volume  = {{26}},
  pages   = {189-206},
  number  = {1},
  year    = {1984}
}

@article{sherman-morrison,
  issn      = {00034851},
  url       = {http://www.jstor.org/stable/2236561},
  author    = {Jack Sherman and Winifred J. Morrison},
  journal   = {The Annals of Mathematical Statistics},
  number    = {1},
  pages     = {124--127},
  publisher = {Institute of Mathematical Statistics},
  title     = {Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix},
  volume    = {21},
  year      = {1950}
}

@article{coresets-strengthened,
  author      = {Tung Mai and
                 Anup B. Rao and
                 Cameron Musco},
  title       = {Coresets for Classification - Simplified and Strengthened},
  journal     = {CoRR},
  volume      = {abs/2106.04254},
  year        = {2021},
  _url        = {https://arxiv.org/abs/2106.04254},
  _eprinttype = {arXiv},
  _eprint     = {2106.04254},
  _timestamp  = {Fri, 11 Jun 2021 11:04:16 +0200},
  _biburl     = {https://dblp.org/rec/journals/corr/abs-2106-04254.bib},
  _bibsource  = {dblp computer science bibliography, https://dblp.org}
}

@book{principal-components,
  series    = {Springer Series in Statistics},
  title     = {Principal Component Analysis},
  publisher = {Springer-Verlag New York},
  author    = {I.T. Jolliffe},
  year      = {2002},
  edition   = {2}
}

@article{bayesian-regression,
  author  = {Leo N. Geppert and
             Katja Ickstadt and
             Alexander Munteanu and
             Jens Quedenfeld and
             Christian Sohler},
  title   = {Random projections for Bayesian regression},
  journal = {Stat. Comput.},
  volume  = {27},
  number  = {1},
  pages   = {79--101},
  year    = {2017}
}

@inproceedings{mmd,
  author    = {Gretton, Arthur and Borgwardt, Karsten and Rasch, Malte and Sch\"{o}lkopf, Bernhard and Smola, Alex},
  booktitle = {Advances in Neural Information Processing Systems},
  editor    = {B. Sch\"{o}lkopf and J. Platt and T. Hoffman},
  pages     = {},
  publisher = {MIT Press},
  title     = {A Kernel Method for the Two-Sample-Problem},
  volume    = {19},
  year      = {2007}
}

@article{woodruff-2014,
  author     = {David P. Woodruff},
  title      = {Sketching as a Tool for Numerical Linear Algebra},
  journal    = {Found. Trends Theor. Comput. Sci.},
  volume     = {10},
  number     = {1-2},
  pages      = {1--157},
  year       = {2014},
  _url       = {https://doi.org/10.1561/0400000060},
  _doi       = {10.1561/0400000060},
  _timestamp = {Thu, 20 Aug 2020 22:50:52 +0200},
  _biburl    = {https://dblp.org/rec/journals/fttcs/Woodruff14.bib},
  _bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{probit-biostatistics,
  author  = {Varin, Cristiano and Czado, Claudia},
  title   = {A mixed autoregressive probit model for ordinal longitudinal data},
  journal = {Biostatistics},
  volume  = {11},
  number  = {1},
  pages   = {127-138},
  year    = {2009},
  month   = {11},
  _issn   = {1465-4644},
  _doi    = {10.1093/biostatistics/kxp042},
  _url    = {https://doi.org/10.1093/biostatistics/kxp042},
  _eprint = {https://academic.oup.com/biostatistics/article-pdf/11/1/127/17732342/kxp042.pdf}
}

@article{probit-econometrics,
  title     = {{B}ayesian estimation of a random effects heteroscedastic probit model},
  author    = {Gu, Yuanyuan and Fiebig, Denzil G and Cripps, Edward and Kohn, Robert},
  journal   = {The Econometrics Journal},
  volume    = {12},
  number    = {2},
  pages     = {324--339},
  year      = {2009},
  publisher = {Oxford University Press Oxford, UK}
}

@article{KalkeR13,
  author    = { S. Kalke and W.-D. Richter },
  title     = {Simulation of the $p$-generalized {G}aussian distribution},
  journal   = {Journal of Statistical Computation and Simulation},
  volume    = {83},
  number    = {4},
  pages     = {641-667},
  year      = {2013},
  publisher = {Taylor & Francis},
  _doi      = {10.1080/00949655.2011.631187},
  _url      = {https://doi.org/10.1080/00949655.2011.631187},
  _eprint   = {https://doi.org/10.1080/00949655.2011.631187}
}