@misc{j-jubran2019,
  title = {Introduction to {{Coresets}}: {{Accurate Coresets}}},
  shorttitle = {Introduction to {{Coresets}}},
  author = {Jubran, Ibrahim and Maalouf, Alaa and Feldman, Dan},
  year = {2019},
  month = oct,
  number = {arXiv:1910.08707},
  eprint = {1910.08707},
  primaryclass = {cs, stat},
  publisher = {{arXiv}},
  doi = {10.48550/arXiv.1910.08707},
  urldate = {2023-05-15},
  abstract = {A coreset (or core-set) of an input set is its small summation, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models, classifiers, loss functions). Over the past decade, coreset construction algorithms have been suggested for many fundamental problems in e.g. machine/deep learning, computer vision, graphics, databases, and theoretical computer science. This introductory paper was written following requests from (usually non-expert, but also colleagues) regarding the many inconsistent coreset definitions, lack of available source code, the required deep theoretical background from different fields, and the dense papers that make it hard for beginners to apply coresets and develop new ones. The paper provides folklore, classic and simple results including step-by-step proofs and figures, for the simplest (accurate) coresets of very basic problems, such as: sum of vectors, minimum enclosing ball, SVD/ PCA and linear regression. Nevertheless, we did not find most of their constructions in the literature. Moreover, we expect that putting them together in a retrospective context would help the reader to grasp modern results that usually extend and generalize these fundamental observations. Experts might appreciate the unified notation and comparison table that links between existing results. Open source code with example scripts are provided for all the presented algorithms, to demonstrate their practical usage, and to support the readers who are more familiar with programming than math.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Computational Geometry,Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/home/iurteaga/snap/zotero-snap/common/Zotero/storage/JLI6MAVH/Jubran et al. - 2019 - Introduction to Coresets Accurate Coresets.pdf;/home/iurteaga/snap/zotero-snap/common/Zotero/storage/SKVKKLP9/1910.html}
}

@article{mehrabi2019survey,
  title={{A} {S}urvey {o}n {B}ias {a}nd {F}airness {i}n {M}achine {L}earning},
  author={Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram},
  journal={{A}CM {C}omputing {S}urveys {(}CSUR)},
  volume={54},
  number={6},
  pages={1--35},
  year={2021},
  publisher={ACM New York, NY, USA}
}


@article{varshney2017safety,
  title={{O}n {t}he {S}afety {o}f {M}achine {L}earning: {C}yber-Physical {S}ystems, {D}ecision {S}ciences, {a}nd {D}ata {P}roducts},
  author={Varshney, Kush R and Alemzadeh, Homa},
  journal={Big data},
  volume={5},
  number={3},
  pages={246--255},
  year={2017},
  publisher={{M}ary {A}nn {L}iebert, {I}nc. {1}40 {H}uguenot {S}treet, {3}rd {F}loor {N}ew {R}ochelle, {N}Y {1}0801 {U}SA}
}

@inproceedings{ribeiro2016should,
  title={{W}hy {S}hould {I} {T}rust {Y}ou?: {E}xplaining {t}he {P}redictions {o}f {A}ny {C}lassifier},
  author={Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  booktitle={{P}roceedings {o}f {t}he {2}2nd {A}CM {S}IGKDD {I}nternational {C}onference {o}n {K}nowledge {D}iscovery {a}nd {D}ata {M}ining},
  pages={1135--1144},
  year={2016}
}

@article{kingma2013auto,
  title={{A}uto-Encoding {V}ariational {B}ayes},
  author={Kingma, Diederik P and Welling, Max},
  journal={International Conference on Learning Representations},
  year={2014}
}

@article{blei2007correlated,
  title={{A} {C}orrelated {T}opic {M}odel {o}f {S}cience},
  author={Blei, David M and Lafferty, John D and others},
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}


@book{bishop2006pattern,
  title={{P}attern {R}ecognition {a}nd {M}achine {L}earning},
  author={Bishop, Christopher M},
  year={2006},
  publisher={springer}
}

@book{gelman2013bayesian,
  title={{B}ayesian {D}ata {A}nalysis},
  author={Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
  year={2013},
  publisher={CRC press}
}

@article{blei2017variational,
  title={{V}ariational {I}nference: {A} {R}eview {f}or {S}tatisticians},
  author={Blei, David M and Kucukelbir, Alp and McAuliffe, Jon D},
  journal={Journal of the American statistical Association},
  volume={112},
  number={518},
  pages={859--877},
  year={2017},
  publisher={Taylor \& Francis}
}

@incollection{jordan1998introduction,
  title={{A}n {I}ntroduction {t}o {V}ariational {M}ethods {f}or {G}raphical {M}odels},
  author={Jordan, Michael I and Ghahramani, Zoubin and Jaakkola, Tommi S and Saul, Lawrence K},
  booktitle={Learning in Graphical Models},
  pages={105--161},
  year={1998},
  publisher={Springer}
}

@article{icu2020data,
  title={{W}iDS ({W}omen {i}n {D}ata {S}cience) {D}atathon {2}020: {I}CU {M}ortality {P}rediction},
  author={Meredith, Meredith and 
  Raffa, Jesse and 
  Ghassemi, Marzyeh and 
  Pollard, Tom and
  Kalanidhi, Sharada and
  Badawi, Omar and
  Matthys, Karen and
  Celi, Leo Anthony 
  },
  journal={Journal of Statistics Education},
  year={2020},
  publisher={PhysioNet}
}

@inproceedings{potts1999,
  title = {Generalized Additive Neural Networks},
  booktitle = {Proceedings of the Fifth {{ACM SIGKDD}} International Conference on {{Knowledge}} Discovery and Data Mining},
  author = {Potts, William J. E.},
  date = {1999-08-01},
  series = {{{KDD}} '99},
  pages = {194--200},
  publisher = {{Association for Computing Machinery}},
  location = {{New York, NY, USA}},
  doi = {10.1145/312129.312228},
  url = {https://doi.org/10.1145/312129.312228},
  urldate = {2023-02-23},
  isbn = {978-1-58113-143-7},
  keywords = {additive models,partial residuals,predictive modeling},
}

@article{agarwal2020neural,
  title={{N}eural {A}dditive {M}odels: {I}nterpretable {M}achine {L}earning {w}ith {N}eural {N}ets},
  author={Agarwal, Rishabh and Frosst, Nicholas and Zhang, Xuezhou and Caruana, Rich and Hinton, Geoffrey E},
  journal={Conference on Neural Information Processing Systems},
  year={2021}
}

@inproceedings{caruana2015intelligible,
  title={{I}ntelligible {M}odels {f}or {H}ealthCare: {P}redicting {P}neumonia {R}isk {a}nd {H}ospital {3}0-day {R}eadmission},
  author={Caruana, Rich and Lou, Yin and Gehrke, Johannes and Koch, Paul and Sturm, Marc and Elhadad, Noemie},
  booktitle={Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining},
  pages={1721--1730},
  year={2015}
}

@article{lecun1998gradient,
  title={{G}radientBased {L}earning {A}pplied {t}o {D}ocument {R}ecognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal={Proceedings of the IEEE},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={Ieee}
}

@inproceedings{chen2018learning,
  title={{L}earning {t}o {E}xplain: {A}n {I}nformation-Theoretic {P}erspective {o}n {M}odel {I}nterpretation},
  author={Chen, Jianbo and Song, Le and Wainwright, Martin and Jordan, Michael},
  booktitle={International Conference on Machine Learning},
  pages={883--892},
  year={2018},
  organization={PMLR}
}

@article{lipton2018mythos,
  title={{T}he {M}ythos {o}f {M}odel {I}nterpretability},
  author={Lipton, Zachary C},
  journal={Queue},
  volume={16},
  number={3},
  pages={31--57},
  year={2018},
  publisher={ACM New York, NY, USA}
}

@inproceedings{jethani2021have,
  title={{H}ave {W}e {L}earned {t}o {E}xplain?: {H}ow {I}nterpretability {M}ethods {C}an {L}earn {t}o {E}ncode {P}redictions {i}n {t}heir {I}nterpretations.},
  author={Jethani, Neil and Sudarshan, Mukund and Aphinyanaphongs, Yindalon and Ranganath, Rajesh},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1459--1467},
  year={2021},
  organization={PMLR}
}

@article{dabkowski2017real,
  title={{R}eal {T}ime {I}mage {S}aliency {f}or {B}lack {B}ox {C}lassifiers},
  author={Dabkowski, Piotr and Gal, Yarin},
  journal={Conference on Neural Information Processing Systems},
  year={2017}
}


@inproceedings{yoon2018invase,
  title={{I}nstance-wise {V}ariable {S}election {u}sing {N}eural {N}etworks},
  author={Yoon, Jinsung and Jordon, James and van der Schaar, Mihaela},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@article{kingma2014adam,
  title={{A}dam: {A} {M}ethod {f}or {S}tochastic {O}ptimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={International Conference for Learning Representations},
  year={2014}
}

@misc{fico,
author = {{FICO}},
title = {FICO Explainable Machine Learning Challenge},
howpublished = {\url{https://community.fico.com/s/explainable-machine-learning-challenge}},
year={2018}
}

@article{goldberger2000physiobank,
  title={{P}hysioBank, {P}hysioToolkit, {a}nd {P}hysioNet: {c}omponents {o}f {a} {n}ew {r}esearch {r}esource {f}or {c}omplex {p}hysiologic {s}ignals},
  author={Goldberger, Ary L and Amaral, Luis AN and Glass, Leon and Hausdorff, Jeffrey M and Ivanov, Plamen Ch and Mark, Roger G and Mietus, Joseph E and Moody, George B and Peng, Chung-Kang and Stanley, H Eugene},
  journal={circulation},
  volume={101},
  number={23},
  pages={e215--e220},
  year={2000},
  publisher={Am Heart Assoc}
}

@article{pedregosa2011scikit,
  title={{S}cikit-learn: {M}achine {l}earning {i}n {P}ython},
  author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011},
  publisher={JMLR. org}
}

@article{friedman2001greedy,
  title={{G}reedy {F}unction {A}pproximation: {A} {G}radient {B}oosting {M}achine},
  author={Friedman, Jerome H},
  journal={Annals of statistics},
  pages={1189--1232},
  year={2001},
  publisher={JSTOR}
}

@inproceedings{chen2016xgboost,
  title={{X}GBoost: {A} {S}calable {T}ree {B}oosting {S}ystem},
  author={Chen, Tianqi and Guestrin, Carlos},
  booktitle={Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={785--794},
  year={2016}
}

@article{nori2019interpretml,
  title={{I}nterpretml: {A} {U}nified {F}ramework {f}or {M}achine {L}earning {I}nterpretability},
  author={Nori, Harsha and Jenkins, Samuel and Koch, Paul and Caruana, Rich},
  journal={arXiv preprint arXiv:1909.09223},
  year={2019}
}

@inproceedings{lou2013accurate,
  title={{A}ccurate {I}ntelligible {M}odels {w}ith {P}airwise {I}nteractions},
  author={Lou, Yin and Caruana, Rich and Gehrke, Johannes and Hooker, Giles},
  booktitle={Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={623--631},
  year={2013}
}

@book{lantz2019machine,
  title={{M}achine {L}earning {w}ith {R}: {E}xpert {t}echniques {f}or {p}redictive {m}odeling},
  author={Lantz, Brett},
  year={2019},
  publisher={Packt publishing ltd}
}

@misc{OECDStat42:online,
author = {OECD},
title = {\textsc{oecd} Statistics},
howpublished = {\url{https://stats.oecd.org/}},
month = {},
year = {2021},
note = {(Accessed on 07/08/2021)}
}

@misc{GHOSearc65:online,
author = {OECD},
title = {\textsc{gho} Search},
howpublished = {\url{https://www.who.int/data/gho/gho-search}},
month = {},
year = {2021},
note = {(Accessed on 07/08/2021)}
}


@misc{UCIMachi51:online,
author = {Janosi, Andras and Steinbrunn, William and Pfisterer, Matthias and Detrano, Robert},
title = {{U}{C}{I} {M}achine {L}earning {R}epository: {H}eart {D}isease {D}ata {S}et},
howpublished = {\url{https://archive.ics.uci.edu/ml/datasets/heart+disease}},
month = {},
year = {},
note = {(Accessed on 07/08/2021)}
}

@Book{Biecek2021,
  author = {Przemyslaw Biecek and Tomasz Burzykowski},
  title = {{{E}xplanatory {M}odel {A}nalysis}},
  publisher = {Chapman and Hall/CRC, New York},
  year = {2021},
  isbn = {9780367135591},
  url = {https://pbiecek.github.io/ema/},
}


@article{alvarez2018robustness,
  title={{O}n {t}he {R}obustness {o}f {I}nterpretability {M}ethods
},
  author={Alvarez-Melis, David and Jaakkola, Tommi S},
  journal={ICML Workshop on Human Interpretability in Machine Learning},
  year={2018}
}

@book{hastie2017generalized,
  title={{G}eneralized {A}dditive {M}odels},
  author={Hastie, Trevor J and Tibshirani, Robert J},
  year={2017},
  publisher={Routledge}
}

@article{clevert2015fast,
  title={{F}ast {a}nd {A}ccurate {D}eep {N}etwork {L}earning {b}y {E}xponential {L}inear {U}nits ({E}{L}{U}s)},
  author={Clevert, Djork-Arn{\'e} and Unterthiner, Thomas and Hochreiter, Sepp},
  journal={International Conference on Learning Representations},
  year={2015}
}

@inproceedings{abadi2016tensorflow,
  title={{T}ensorFlow: {A} {S}ystem {f}or {L}arge-Scale {M}achine {L}earning},
  author={Abadi, Mart{\'\i}n and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and others},
  booktitle={12th $\{$USENIX$\}$ Symposium on Operating Systems Design and Implementation ($\{$OSDI$\}$ 16)},
  pages={265--283},
  year={2016}
}

@article{stone1974cross,
  title={{C}ross-Validatory {C}hoice {a}nd {A}ssessment {o}f {S}tatistical {P}redictions},
  author={Stone, Mervyn},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={36},
  number={2},
  pages={111--133},
  year={1974},
  publisher={Wiley Online Library}
}

@inproceedings{lundberg2017unified,
  title={{A} {U}nified {A}pproach {t}o {I}nterpreting {M}odel {P}redictions},
  author={Lundberg, Scott M and Lee, Su-In},
  booktitle={Proceedings of the 31st International Conference on Neural Information Processing Systems},
  pages={4768--4777},
  year={2017}
}

@inproceedings{shrikumar2017learning,
  title={{L}earning {I}mportant {F}eatures {T}hrough {P}ropagating {A}ctivation {D}ifferences},
  author={Shrikumar, Avanti and Greenside, Peyton and Kundaje, Anshul},
  booktitle={International Conference on Machine Learning},
  pages={3145--3153},
  year={2017},
  organization={PMLR}
}

@article{rudin2021interpretable,
  title={{I}nterpretable {M}achine {L}earning: {F}undamental {P}rinciples {a}nd {1}0 {G}rand {C}hallenges},
  author={Rudin, Cynthia and Chen, Chaofan and Chen, Zhi and Huang, Haiyang and Semenova, Lesia and Zhong, Chudi},
  journal={Statistics Surveys},
  year={2021}
}

@article{ribeiro2016model,
  title={{M}odel-Agnostic {I}nterpretability {o}f {M}achine {L}earning},
  author={Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  journal={ICML Workshop on Human Interpretability in Machine Learning},
  year={2016}
}


@article{goldstein2015peeking,
  title={{P}eeking {I}nside {t}he {B}lack {B}ox: {V}isualizing {S}tatistical {L}earning {w}ith {P}lots {o}f {I}ndividual {C}onditional {E}xpectation},
  author={Goldstein, Alex and Kapelner, Adam and Bleich, Justin and Pitkin, Emil},
  journal={journal of Computational and Graphical Statistics},
  volume={24},
  number={1},
  pages={44--65},
  year={2015},
  publisher={Taylor \& Francis}
}

@article{apley2020visualizing,
  title={{V}isualizing {t}he {E}ffects {o}f {P}redictor {V}ariables {i}n {B}lack {B}ox {S}upervised {L}earning {M}odels},
  author={Apley, Daniel W and Zhu, Jingyu},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={82},
  number={4},
  pages={1059--1086},
  year={2020},
  publisher={Wiley Online Library}
}

@inproceedings{ioffe2015batch,
  title={{B}atch {N}ormalization: {A}ccelerating {D}eep {N}etwork {T}raining {b}y {R}educing {I}nternal {C}ovariate {S}hift},
  author={Ioffe, Sergey and Szegedy, Christian},
  booktitle={International Conference on Machine Learning},
  pages={448--456},
  year={2015},
  organization={PMLR}
}

@article{ba2016layer,
  title={{L}ayer {N}ormalization},
  author={Ba, Jimmy Lei and Kiros, Jamie Ryan and Hinton, Geoffrey E},
  journal={arXiv preprint arXiv:1607.06450},
  year={2016}
}

@book{gelman1995bayesian,
  title={{B}ayesian {D}ata {A}nalysis},
  author={Gelman, Andrew and Carlin, John B and Stern, Hal S and Rubin, Donald B},
  year={1995},
  publisher={Chapman and Hall/CRC}
}

@book{molnar2019, title = {{I}nterpretable {M}achine {L}earning}, author = {Christoph Molnar}, year = {2019}, subtitle = {A Guide for Making Black Box Models Explainable} }

@article{yamamoto2013health,
  title={{H}ealth {C}are {C}osts—{F}rom {B}irth to {D}eath},
  author={Yamamoto, Dale H},
  journal={Health Care Cost Institute Report},
  volume={1},
  year={2013}
}

@article{an2015health,
  title={{H}ealth {C}are {E}xpenses {i}n {R}elation {t}o {O}besity {a}nd {S}moking {A}mong {U}S {A}dults {b}y {G}ender, {R}ace/Ethnicity, {a}nd {A}ge {G}roup: {1}998--2011},
  author={An, Ruopeng},
  journal={Public health},
  volume={129},
  number={1},
  pages={29--36},
  year={2015},
  publisher={Elsevier}
}

@article{loeb1984smoking,
  title={{S}moking {a}nd {L}ung {C}ancer: {A}n {O}verview},
  author={Loeb, Lawrence A and Emster, Virginia L and Warner, Kenneth E and Abbotts, John and Laszlo, John},
  journal={Cancer Research},
  volume={44},
  number={12 Part 1},
  pages={5940--5958},
  year={1984},
  publisher={AACR}
}

@article{gandini2008tobacco,
  title={{T}obacco {S}moking {a}nd {C}ancer: {A} {M}eta-Analysis},
  author={Gandini, Sara and Botteri, Edoardo and Iodice, Simona and Boniol, Mathieu and Lowenfels, Albert B and Maisonneuve, Patrick and Boyle, Peter},
  journal={International journal of cancer},
  volume={122},
  number={1},
  pages={155--164},
  year={2008},
  publisher={Wiley Online Library}
}

@inproceedings{lou2012intelligible,
  title={Intelligible models for classification and regression},
  author={Lou, Yin and Caruana, Rich and Gehrke, Johannes},
  booktitle={Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={150--158},
  year={2012}
}

@book{glance2019,
   author = "OECD",
   title = "{H}ealth {a}t {a} {G}lance {2}019",
   year = "2019",
   pages = 243,
   url = "https://www.oecd-ilibrary.org/content/publication/4dd50c09-en",
   doi = "https://doi.org/https://doi.org/10.1787/4dd50c09-en" 
}

@inproceedings{wilson2016,
  title = {Deep {{Kernel Learning}}},
  booktitle = {Proceedings of the 19th {{International Conference}} on {{Artificial Intelligence}} and {{Statistics}}},
  author = {Wilson, Andrew Gordon and Hu, Zhiting and Salakhutdinov, Ruslan and Xing, Eric P.},
  date = {2016-05-02},
  pages = {370--378},
  publisher = {{PMLR}},
  issn = {1938-7228},
  url = {https://proceedings.mlr.press/v51/wilson16.html},
  urldate = {2023-02-24},
  abstract = {We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods.  Specifically, we transform the inputs of a spectral mixture base kernel with a deep architecture, using local kernel interpolation, inducing points, and structure exploiting (Kronecker and Toeplitz) algebra for a scalable kernel representation.  These closed-form kernels can be used as drop-in replacements for standard kernels, with benefits in expressive power and scalability.  We jointly learn the properties of these kernels through the marginal likelihood of a Gaussian process.  Inference and learning cost O(n) for n training points, and predictions cost O(1) per test point.  On a large and diverse collection of applications, including a dataset with 2 million examples, we show improved performance over scalable Gaussian processes with flexible kernel learning models, and stand-alone deep architectures.},
  eventtitle = {Artificial {{Intelligence}} and {{Statistics}}},
  langid = {english},
  file = {/home/iurteaga/snap/zotero-snap/common/Zotero/storage/8R64WI54/Wilson et al. - 2016 - Deep Kernel Learning.pdf}
}


@inproceedings{dai2020interpretable,
  title={An interpretable and sample efficient deep kernel for Gaussian process},
  author={Dai, Yijue and Zhang, Tianjian and Lin, Zhidi and Yin, Feng and Theodoridis, Sergios and Cui, Shuguang},
  booktitle={Conference on Uncertainty in Artificial Intelligence},
  pages={759--768},
  year={2020},
  organization={PMLR}
}

@book{rasmussen2006gaussian,
  title={Gaussian processes for machine learning},
  author={Rasmussen, Carl Edward and Williams, Christopher KI and others},
  volume={1},
  year={2006},
  publisher={Springer}
}

@article{remes2017non,
  title={Non-stationary spectral kernels},
  author={Remes, Sami and Heinonen, Markus and Kaski, Samuel},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{kuss2003gaussian,
  title={Gaussian processes in reinforcement learning},
  author={Kuss, Malte and Rasmussen, Carl},
  journal={Advances in neural information processing systems},
  volume={16},
  year={2003}
}

@inproceedings{cohn2013modelling,
  title={Modelling annotator bias with multi-task gaussian processes: An application to machine translation quality estimation},
  author={Cohn, Trevor and Specia, Lucia},
  booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={32--42},
  year={2013}
}

@inproceedings{liu2019probabilistic,
  title={Probabilistic deep ordinal regression based on gaussian processes},
  author={Liu, Yanzhu and Wang, Fan and Kong, Adams Wai Kin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={5301--5309},
  year={2019}
}

@incollection{hastie1992,
  title = {Generalized {{Additive Models}}},
  booktitle = {Statistical {{Models}} in {{S}}},
  author = {Hastie, Trevor J.},
  date = {1992},
  publisher = {{Routledge}},
  abstract = {This chapter discusses the innovations of additional flexible methods for modeling an individual term in an additive model. It focuses on how we fit additive models. A general and efficient algorithm for fitting a generalized additive model consists of a hierarchy of three modules: scatterplot smoothers, backfitting algorithm, and local-scoring algorithm. These three steps are a rather natural and intuitive generalization of the usual linear model algorithms, and that is how they were originally conceived. The algorithm for fitting a gam is exactly analogous to the algorithm for glms. The chapter presents the S functions for fitting and understanding generalized additive models. In some cases, especially for generalized linear or additive models, adding residuals to a plot is unhelpful because they can distort the scale dramatically. Any interesting features in the functions get lost because of a few large residuals, even though they may carry a very small weight.},
  isbn = {978-0-203-73853-5},
  pagetotal = {59}
}

@article{duvenaud2011additive,
  title={Additive gaussian processes},
  author={Duvenaud, David K and Nickisch, Hannes and Rasmussen, Carl},
  journal={Advances in neural information processing systems},
  volume={24},
  year={2011}
}

@inproceedings{lu2022additive,
  title={Additive Gaussian Processes Revisited},
  author={Lu, Xiaoyu and Boukouvalas, Alexis and Hensman, James},
  booktitle={International Conference on Machine Learning},
  pages={14358--14383},
  year={2022},
  organization={PMLR}
}

@article{plate1999accuracy,
  title={Accuracy versus interpretability in flexible modeling: Implementing a tradeoff using gaussian process models},
  author={Plate, Tony A},
  journal={Behaviormetrika},
  volume={26},
  pages={29--50},
  year={1999},
  publisher={Springer}
}

@article{tsang2017detecting,
  title={Detecting statistical interactions from neural network weights},
  author={Tsang, Michael and Cheng, Dehua and Liu, Yan},
  journal={arXiv preprint arXiv:1705.04977},
  year={2017}
}

@inproceedings{germain2015made,
  title={Made: Masked autoencoder for distribution estimation},
  author={Germain, Mathieu and Gregor, Karol and Murray, Iain and Larochelle, Hugo},
  booktitle={International conference on machine learning},
  pages={881--889},
  year={2015},
  organization={PMLR}
}

@article{lachapelle2019gradient,
  title={Gradient-based neural dag learning},
  author={Lachapelle, S{\'e}bastien and Brouillard, Philippe and Deleu, Tristan and Lacoste-Julien, Simon},
  journal={arXiv preprint arXiv:1906.02226},
  year={2019}
}

@inproceedings{cremer2018inference,
  title={Inference suboptimality in variational autoencoders},
  author={Cremer, Chris and Li, Xuechen and Duvenaud, David},
  booktitle={International Conference on Machine Learning},
  pages={1078--1086},
  year={2018},
  organization={PMLR}
}

@article{hensman2013gaussian,
  title={Gaussian processes for big data},
  author={Hensman, James and Fusi, Nicolo and Lawrence, Neil D},
  journal={Uncertainty in Artificial Intelligence},
  year={2013}
}

@inproceedings{wilson2016deep,
  title={Deep kernel learning},
  author={Wilson, Andrew Gordon and Hu, Zhiting and Salakhutdinov, Ruslan and Xing, Eric P},
  booktitle={Artificial intelligence and statistics},
  pages={370--378},
  year={2016},
  organization={PMLR}
}

@article{snelson2005sparse,
  title={Sparse Gaussian processes using pseudo-inputs},
  author={Snelson, Edward and Ghahramani, Zoubin},
  journal={Advances in neural information processing systems},
  volume={18},
  year={2005}
}

@article{yuan2012,
  title = {Sparse-Posterior {{Gaussian Processes}} for General Likelihoods},
  author = {Yuan and Qi and Abdel-Gawad, Ahmed H. and Minka, Thomas P.},
  year = {2012},
  journal={arXiv preprint arXiv:1203.3507},
  doi = {10.48550/arXiv.1203.3507},
  url = {http://arxiv.org/abs/1203.3507},
  abstract = {Gaussian processes (GPs) provide a probabilistic nonparametric representation of functions in regression, classification, and other problems. Unfortunately, exact learning with GPs is intractable for large datasets. A variety of approximate GP methods have been proposed that essentially map the large dataset into a small set of basis points. Among them, two state-of-the-art methods are sparse pseudo-input Gaussian process (SPGP) (Snelson and Ghahramani, 2006) and variablesigma GP (VSGP) Walder et al. (2008), which generalizes SPGP and allows each basis point to have its own length scale. However, VSGP was only derived for regression. In this paper, we propose a new sparse GP framework that uses expectation propagation to directly approximate general GP likelihoods using a sparse and smooth basis. It includes both SPGP and VSGP for regression as special cases. Plus as an EP algorithm, it inherits the ability to process data online. As a particular choice of approximating family, we blur each basis point with a Gaussian distribution that has a full covariance matrix representing the data distribution around that basis point; as a result, we can summarize local data manifold information with a small set of basis points. Our experiments demonstrate that this framework outperforms previous GP classification methods on benchmark datasets in terms of minimizing divergence to the non-sparse GP solution as well as lower misclassification rate.},
  pubstate = {preprint},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
}

@article{bui2017,
  title = {A {{Unifying Framework}} for {{Gaussian Process Pseudo-Point Approximations}} Using {{Power Expectation Propagation}}},
  author = {Bui, Thang D. and Yan, Josiah and Turner, Richard E.},
  year = {2017},
  journal = {Journal of Machine Learning Research},
  volume = {18},
  number = {104},
  pages = {1--72},
  issn = {1533-7928},
  url = {http://jmlr.org/papers/v18/16-603.html},
}

@inproceedings{wilson2015kernel,
  title={Kernel interpolation for scalable structured Gaussian processes (KISS-GP)},
  author={Wilson, Andrew and Nickisch, Hannes},
  booktitle={International conference on machine learning},
  pages={1775--1784},
  year={2015},
  organization={PMLR}
}

@article{lucic2017training,
  title={Training gaussian mixture models at scale via coresets},
  author={Lucic, Mario and Faulkner, Matthew and Krause, Andreas and Feldman, Dan},
  journal={The Journal of Machine Learning Research},
  volume={18},
  number={1},
  pages={5885--5909},
  year={2017},
  publisher={JMLR. org}
}

@article{huggins2016coresets,
  title={Coresets for scalable Bayesian logistic regression},
  author={Huggins, Jonathan and Campbell, Trevor and Broderick, Tamara},
  journal={Advances in Neural Information Processing Systems},
  volume={29},
  year={2016}
}

@inproceedings{campbell2018bayesian,
  title={Bayesian coreset construction via greedy iterative geodesic ascent},
  author={Campbell, Trevor and Broderick, Tamara},
  booktitle={International Conference on Machine Learning},
  pages={698--706},
  year={2018},
  organization={PMLR}
}

@article{campbell2019automated,
  title={Automated scalable Bayesian inference via Hilbert coresets},
  author={Campbell, Trevor and Broderick, Tamara},
  journal={The Journal of Machine Learning Research},
  volume={20},
  number={1},
  pages={551--588},
  year={2019},
  publisher={JMLR. org}
}

@inproceedings{zhang2021bayesian,
  title={Bayesian coresets: Revisiting the nonconvex optimization perspective},
  author={Zhang, Jacky and Khanna, Rajiv and Kyrillidis, Anastasios and Koyejo, Sanmi},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={2782--2790},
  year={2021},
  organization={PMLR}
}

@article{campbell2019sparse,
  title={Sparse variational inference: Bayesian coresets from scratch},
  author={Campbell, Trevor and Beronov, Boyan},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}

@inproceedings{titsias2009variational,
  title={Variational learning of inducing variables in sparse Gaussian processes},
  author={Titsias, Michalis},
  booktitle={Artificial intelligence and statistics},
  pages={567--574},
  year={2009},
  organization={PMLR}
}

@article{williams2000using,
  title={Using the Nystr{\"o}m method to speed up kernel machines},
  author={Williams, Christopher and Seeger, Matthias},
  journal={Advances in neural information processing systems},
  volume={13},
  year={2000}
}

@article{quinonero2005unifying,
  title={A unifying view of sparse approximate Gaussian process regression},
  author={Quinonero-Candela, Joaquin and Rasmussen, Carl Edward},
  journal={The Journal of Machine Learning Research},
  volume={6},
  pages={1939--1959},
  year={2005},
  publisher={JMLR. org}
}


@article{naish2007generalized,
  title={The generalized FITC approximation},
  author={Naish-Guzman, Andrew and Holden, Sean},
  journal={Advances in neural information processing systems},
  volume={20},
  year={2007}
}

@article{gardner2018gpytorch,
  title={Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration},
  author={Gardner, Jacob and Pleiss, Geoff and Weinberger, Kilian Q and Bindel, David and Wilson, Andrew G},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@article{burt2020gpviconv,
  author  = {David R. Burt and Carl Edward Rasmussen and Mark van der Wilk},
  title   = {Convergence of Sparse Variational Inference in Gaussian Processes Regression},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {131},
  pages   = {1-63},
  url     = {http://jmlr.org/papers/v21/19-1015.html}
}

@InProceedings{burt2019gpviconv,
  title = 	 {Rates of Convergence for Sparse Variational {G}aussian Process Regression},
  author = 	 {Burt, David and Rasmussen, Carl Edward and van der Wilk, Mark},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {862--871},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Long Beach, California, USA},
  month = 	 {09--15 Jun},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/burt19a/burt19a.pdf},
  url = 	 {http://proceedings.mlr.press/v97/burt19a.html},
}

@article{csato2002sparse,
  title={Sparse on-line Gaussian processes},
  author={Csat{\'o}, Lehel and Opper, Manfred},
  journal={Neural computation},
  volume={14},
  number={3},
  pages={641--668},
  year={2002},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

@inproceedings{smola2000,
  title = {Sparse {{Greedy Gaussian Process Regression}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Smola, Alex and Bartlett, Peter},
  year = {2000},
  volume = {13},
  publisher = {{MIT Press}},
  urldate = {2023-08-04},
  abstract = {We  present  a  simple  sparse  greedy  technique  to  approximate  the  maximum a  posteriori estimate of Gaussian Processes  with much  improved  scaling  behaviour  in  the  sample  size  m.  In  particular,  computational  requirements  are  O(n2m),  storage  is  O(nm),  the  cost  for  prediction  is  0 ( n)  and  the  cost  to  compute  confidence  bounds  is  O(nm),  where  n  \guillemotleft :  m.  We  show  how  to  compute  a  stopping  criterion,  give  bounds  on  the  approximation  error,  and  show applications to large scale problems.},
  file = {/home/iurteaga/snap/zotero-snap/common/Zotero/storage/RSV5J9ZQ/Smola and Bartlett - 2000 - Sparse Greedy Gaussian Process Regression.pdf}
}

@inproceedings{seeger2003fast,
  title={Fast forward selection to speed up sparse Gaussian process regression},
  author={Seeger, Matthias W and Williams, Christopher KI and Lawrence, Neil D},
  booktitle={International Workshop on Artificial Intelligence and Statistics},
  pages={254--261},
  year={2003},
  organization={PMLR}
}

@inproceedings{keerthi2005,
  title = {A Matching Pursuit Approach to Sparse {{Gaussian}} Process Regression},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Keerthi, Sathiya and Chu, Wei},
  year = {2005},
  volume = {18},
  publisher = {{MIT Press}},
  urldate = {2023-10-04},
  abstract = {In this paper we propose a new basis selection criterion for building sparse GP regression models that provides promising gains in accuracy as well as efficiency over previous methods. Our algorithm is much faster than that of Smola and Bartlett, while, in generalization it greatly outperforms the information gain approach proposed by Seeger et al, especially on the quality of predictive distributions.},
  file = {/home/iurteaga/snap/zotero-snap/common/Zotero/storage/IR8PTAPD/Keerthi and Chu - 2005 - A matching pursuit approach to sparse Gaussian pro.pdf}
}

@book{snelson2008flexible,
  title={Flexible and efficient Gaussian process models for machine learning},
  author={Snelson, Edward Lloyd},
  year={2008},
  publisher={University of London, University College London (United Kingdom)}
}

@article{liu2020gaussian,
  title={When Gaussian process meets big data: A review of scalable GPs},
  author={Liu, Haitao and Ong, Yew-Soon and Shen, Xiaobo and Cai, Jianfei},
  journal={IEEE transactions on neural networks and learning systems},
  volume={31},
  number={11},
  pages={4405--4423},
  year={2020},
  publisher={IEEE}
}

@phdthesis{Matthews2017,
  title={Scalable Gaussian process inference using variational methods},
  author={de Garis Matthews, Alexander Graeme},
  year={2017},
  school={University of Cambridge}
}

@inproceedings{bauer2016,
  title = {Understanding {{Probabilistic Sparse Gaussian Process Approximations}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Bauer, Matthias and {van der Wilk}, Mark and Rasmussen, Carl Edward},
  year = {2016},
  volume = {29},
  publisher = {{Curran Associates, Inc.}},
  urldate = {2023-02-28},
  abstract = {Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical properties and behave differently in practice. We thoroughly investigate the two methods for regression both analytically and through illustrative examples, and draw conclusions to guide practical application.},
  file = {/home/iurteaga/snap/zotero-snap/common/Zotero/storage/9EX8MVMU/Bauer et al. - 2016 - Understanding Probabilistic Sparse Gaussian Proces.pdf}
}

@misc{asuncion2007uci,
  title={UCI machine learning repository},
  author={Asuncion, Arthur and Newman, David},
  year={2007},
  publisher={Irvine, CA, USA}
}

@inproceedings{jankowiak2020parametric,
  title={Parametric gaussian process regressors},
  author={Jankowiak, Martin and Pleiss, Geoff and Gardner, Jacob},
  booktitle={International Conference on Machine Learning},
  pages={4702--4712},
  year={2020},
  organization={PMLR}
}

@article{hartigan1979algorithm,
  title={Algorithm AS 136: A k-means clustering algorithm},
  author={Hartigan, John A and Wong, Manchek A},
  journal={Journal of the royal statistical society. series c (applied statistics)},
  volume={28},
  number={1},
  pages={100--108},
  year={1979},
  publisher={JSTOR}
}


@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@article{troyanskaya2001missing,
  title={Missing value estimation methods for DNA microarrays},
  author={Troyanskaya, Olga and Cantor, Michael and Sherlock, Gavin and Brown, Pat and Hastie, Trevor and Tibshirani, Robert and Botstein, David and Altman, Russ B},
  journal={Bioinformatics},
  volume={17},
  number={6},
  pages={520--525},
  year={2001},
  publisher={Oxford University Press}
}

@article{bike,
year={2013},
issn={2192-6352},
journal={Progress in Artificial Intelligence},
doi={10.1007/s13748-013-0040-3},
title={Event labeling combining ensemble detectors and background knowledge},
url={[Web Link]},
publisher={Springer Berlin Heidelberg},
keywords={Event labeling; Event detection; Ensemble learning; Background knowledge},
author={Fanaee-T, Hadi and Gama, Joao},
pages={1-15}
}

@article{little2007exploiting,
  title={Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection},
  author={Little, Max and Mcsharry, Patrick and Roberts, Stephen and Costello, Declan and Moroz, Irene},
  journal={Nature Precedings},
  pages={1--1},
  year={2007},
  publisher={Nature Publishing Group UK London}
}

@article{thompson2013video,
  title={Video game telemetry as a critical tool in the study of complex skill learning},
  author={Thompson, Joseph J and Blair, Mark R and Chen, Lihan and Henrey, Andrew J},
  journal={PloS one},
  volume={8},
  number={9},
  pages={e75129},
  year={2013},
  publisher={Public Library of Science San Francisco, USA}
}

@article{rana2013physicochemical,
  title={Physicochemical properties of protein tertiary structure data set},
  author={Rana, Prashant S},
  journal={UCI Machine Learning Repository},
  year={2013}
}

@article{cortez2009modeling,
  title={Modeling wine preferences by data mining from physicochemical properties},
  author={Cortez, Paulo and Cerdeira, Ant{\'o}nio and Almeida, Fernando and Matos, Telmo and Reis, Jos{\'e}},
  journal={Decision support systems},
  volume={47},
  number={4},
  pages={547--553},
  year={2009},
  publisher={Elsevier}
}

@article{paszke2019pytorch,
  title={Pytorch: An imperative style, high-performance deep learning library},
  author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{manousakas2022black,
  title={Black-box coreset variational inference},
  author={Manousakas, Dionysis and Ritter, Hippolyt and Karaletsos, Theofanis},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={34175--34187},
  year={2022}
}

@article{manousakas2020bayesian,
  title={Bayesian pseudocoresets},
  author={Manousakas, Dionysis and Xu, Zuheng and Mascolo, Cecilia and Campbell, Trevor},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={14950--14960},
  year={2020}
}

@misc{year_prediction_msd_203,
  author       = {Bertin-Mahieux, T.},
  title        = {{Year Prediction MSD}},
  year         = {2011},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C50K61}
}

@article{graf2011relative,
  title={Relative location of ct slices on axial axis data set},
  author={Graf, F and Kriegel, HP and Schubert, M and Poelsterl, S and Cavallaro, A},
  journal={UCI Mach. Learn. Repository},
  year={2011}
}