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  author        = {Hospedales, Timothy M and Antoniou, Antreas and Micaelli, Paul and Storkey, Amos J},
  year          = 2020,
  journal       = {Trans. PAMI},
}
@inproceedings{neuraltangents2020,
  title         = {{Neural tangents: Fast and easy infinite neural networks in Python}},
  author        = {Roman Novak and Lechao Xiao and Jiri Hron and Jaehoon Lee and Alexander A. Alemi and Jascha Sohl-Dickstein and Samuel S. Schoenholz},
  year          = 2020,
  booktitle     = {Proc. ICLR},
}
@misc{predictinggeneralization2020,
  title         = {{NeurIPS 2020 Competition: Predicting generalization in deep learning}},
  author        = {Jiang, Yiding and Foret, Pierre and Yak, Scott and Roy, Daniel M. and Mobahi, Hossein and Dziugaite, Gintare Karolina and Bengio, Samy and Gunasekar, Suriya and Guyon, Isabelle and Neyshabur, Behnam},
  year          = 2020,
  publisher     = {arXiv},
}
@inproceedings{smith2020origin,
  title         = {{On the origin of implicit regularization in stochastic gradient descent}},
  author        = {Smith, Samuel L and Dherin, Benoit and Barrett, David and De, Soham},
  year          = 2020,
  booktitle     = {Proc. ICLR},
}
@article{geirhos2020shortcut,
  title         = {{Shortcut learning in deep neural networks}},
  author        = {Geirhos, Robert and Jacobsen, J{\"o}rn-Henrik and Michaelis, Claudio and Zemel, Richard and Brendel, Wieland and Bethge, Matthias and Wichmann, Felix A},
  year          = 2020,
  journal       = {Nat. Mach. Intell.},
  volume        = 2,
  pages         = {665--673},
}
@inproceedings{liu2020ahgp,
  title         = {{Task-agnostic amortized inference of Gaussian process hyperparameters}},
  author        = {Liu, Sulin and Sun, Xingyuan and Ramadge, Peter J and Adams, Ryan P.},
  year          = 2020,
  booktitle     = {Adv. NeurIPS},
}
@article{Sejnowski2020TheUE,
  title         = {{The unreasonable effectiveness of deep learning in artificial intelligence}},
  author        = {Terrence J. Sejnowski},
  year          = 2020,
  journal       = {PNAS},
  volume        = 117,
}
@article{d2020underspecification,
  title         = {{Underspecification presents challenges for credibility in modern machine learning}},
  author        = {D'Amour, Alexander and Heller, Katherine and Moldovan, Dan and Adlam, Ben and Alipanahi, Babak and Beutel, Alex and Chen, Christina and Deaton, Jonathan and Eisenstein, Jacob and Hoffman, Matthew D and others},
  year          = 2020,
  eprint        = {2011.03395},
  archiveprefix = {arXiv},
}
@inproceedings{xu2021how,
  title         = {{How neural networks extrapolate: From feedforward to graph neural networks}},
  author        = {Keyulu Xu and Mozhi Zhang and Jingling Li and Simon Shaolei Du and Ken-Ichi Kawarabayashi and Stefanie Jegelka},
  year          = 2021,
  booktitle     = {Proc. ICLR},
}
@inproceedings{simpson2021kernel,
  title         = {{Kernel identification through transformers}},
  author        = {Fergus Simpson and Ian Davies and Vidhi Lalchand and Alessandro Vullo and Nicolas Durrande and Carl Edward Rasmussen},
  year          = 2021,
  booktitle     = {Adv. NeurIPS},
}
@inproceedings{simpson2021marginalised,
  title         = {{Marginalised Gaussian processes with nested sampling}},
  author        = {Fergus Simpson and Vidhi Lalchand and Carl Edward Rasmussen},
  year          = 2021,
  booktitle     = {Adv. NeurIPS},
  editor        = {A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
}
@article{stephenson2021measuring,
  title         = {{Measuring the sensitivity of Gaussian processes to kernel choice}},
  author        = {Stephenson, William T and Ghosh, Soumya and Nguyen, Tin D and Yurochkin, Mikhail and Deshpande, Sameer K and Broderick, Tamara},
  year          = 2021,
  journal       = {arXiv preprint arXiv:2106.06510},
}
@article{bommasani2021opportunities,
  title         = {{On the opportunities and risks of foundation models}},
  author        = {Rishi Bommasani and others},
  year          = 2021,
  eprint        = {2108.07258},
  archiveprefix = {arXiv},
}
@inproceedings{pmlr-v139-immer21a,
  title         = {{Scalable marginal likelihood estimation for model selection in deep learning}},
  author        = {Immer, Alexander and Bauer, Matthias and Fortuin, Vincent and R{\"a}tsch, Gunnar and Emtiyaz, Khan Mohammad},
  year          = 2021,
  month         = {07},
  booktitle     = {Proc. ICML},
  editor        = {Meila, Marina and Zhang, Tong},
}
@article{dehghani2021benchmark,
  title         = {{The benchmark lottery}},
  author        = {Dehghani, Mostafa and Tay, Yi and Gritsenko, Alexey A and Zhao, Zhe and Houlsby, Neil and Diaz, Fernando and Metzler, Donald and Vinyals, Oriol},
  year          = 2021,
  archiveprefix = {arXiv},
  eprint        = {2107.07002},
}
@phdthesis{nakkiran2021towards,
  title         = {{Towards an empirical theory of deep learning}},
  author        = {Nakkiran, Preetum},
  year          = 2021,
  school        = {Harvard University},
}
@article{ortiz2021can,
  title         = {{What can linearized neural networks actually say about generalization?}},
  author        = {Ortiz-Jim{\'e}nez, Guillermo and Moosavi-Dezfooli, Seyed-Mohsen and Frossard, Pascal},
  year          = 2021,
  journal       = {Adv. NeurIPS},
  volume        = 34,
}
@inproceedings{jiang2022assessing,
  title         = {{Assessing generalization of SGD via disagreement}},
  author        = {Yiding Jiang and Vaishnavh Nagarajan and Christina Baek and J Zico Kolter},
  year          = 2022,
  booktitle     = {Proc. ICLR},
}
@inproceedings{lotfi2022bayesian,
  title         = {{Bayesian model selection, the marginal likelihood, and generalization}},
  author        = {Lotfi, Sanae and Izmailov, Pavel and Benton, Gregory and Goldblum, Micah and Wilson, Andrew Gordon},
  year          = 2022,
  journal       = {Proc. ICML},
}
@inproceedings{ilyas2022datamodels,
  title         = {{Datamodels: Understanding predictions with data and data with predictions}},
  author        = {Ilyas, Andrew and Park, Sung Min and Engstrom, Logan and Leclerc, Guillaume and Madry, Aleksander},
  year          = 2022,
  booktitle     = {Proc. ICML},
}
@inproceedings{zhou2022exsum,
  title         = {{ExSum: From local explanations to model understanding}},
  author        = {Zhou, Yilun and Ribeiro, Marco Tulio and Shah, Julie},
  year          = 2022,
  booktitle     = {Proc. NAACL},
}
@article{vyas2022limitations,
  title         = {{Limitations of the NTK for understanding generalization in deep learning}},
  author        = {Vyas, Nikhil and Bansal, Yamini and Nakkiran, Preetum},
  year          = 2022,
  journal       = {arXiv preprint arXiv:2206.10012},
}
@inproceedings{sellam2022the,
  title         = {{The MultiBERTs: BERT reproductions for robustness analysis}},
  author        = {Thibault Sellam and Steve Yadlowsky and Ian Tenney and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Raluca Turc and Jacob Eisenstein and Dipanjan Das and Ellie Pavlick},
  year          = 2022,
  booktitle     = {Proc. ICLR},
}
@book{PDLT-2022,
  title         = {{The principles of deep learning theory}},
  author        = {Roberts, Daniel A. and Yaida, Sho and Hanin, Boris},
  year          = 2022,
  publisher     = {Cambridge University Press},
  eprint        = {2106.10165},
  archiveprefix = {arXiv},
}

@inproceedings{Lotfi2022BayesianMS,
  title={Bayesian Model Selection, the Marginal Likelihood, and Generalization},
  author={Sanae Lotfi and Pavel Izmailov and Gregory W. Benton and Micah Goldblum and Andrew Gordon Wilson},
  booktitle={ICML},
  year={2022}
}

@inproceedings{Wang2019ExactGP,
  title={{Exact Gaussian processes on a million data points}},
  author={Ke Alexander Wang and Geoff Pleiss and Jacob R. Gardner and Stephen Tyree and Kilian Q. Weinberger and Andrew Gordon Wilson},
  booktitle={Adv. NeurIPS},
  year={2019}
}
@inproceedings{ffsletnns,
 author = {Tancik, Matthew and Srinivasan, Pratul and Mildenhall, Ben and Fridovich-Keil, Sara and Raghavan, Nithin and Singhal, Utkarsh and Ramamoorthi, Ravi and Barron, Jonathan and Ng, Ren},
 booktitle = {Adv. NeurIPS},
 pages = {7537--7547},
 title = {Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains},
 year = {2020}
}


@InProceedings{kohinfluence,
  title = 	 {Understanding Black-box Predictions via Influence Functions},
  author =       {Pang Wei Koh and Percy Liang},
  booktitle = 	 {Proc. 34th ICML},
  year = 	 {2017},
  editor = 	 {Precup, Doina and Teh, Yee Whye},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--11 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/koh17a/koh17a.pdf},
  abstract = 	 {How can we explain the predictions of a black-box model? In this paper, we use influence functions — a classic technique from robust statistics — to trace a model’s prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks.}
}


@inproceedings{fastcvgp2021,
  title={Fast calculation of {G}aussian {P}rocess multiple-fold cross-validation residuals and their covariances},
  author={David Ginsbourger and Cedric Scharer},
  year={2021}
}

@article{xiongmetamodel,
author = {Xiong, Ying and Chen, Wei and Apley, Daniel and Ding, Xuru},
title = {A non-stationary covariance-based Kriging method for metamodelling in engineering design},
journal = {Int. J. Numer. Meth. Eng.},
volume = {71},
number = {6},
year = {2007}
}


@inproceedings{mildenhall2020nerf,
 title={{NeRF}: Representing Scenes as Neural Radiance Fields for View Synthesis},
 author={Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng},
 year={2020},
 booktitle={ECCV},
}

@article{Dubrule1983CrossVO,
  title={Cross validation of kriging in a unique neighborhood},
  author={Olivier Dubrule},
  journal={Journal of the International Association for Mathematical Geology},
  year={1983},
  volume={15},
}


@InProceedings{pmlr-v5-titsias09a,
  title = 	 {{Variational learning of inducing variables in sparse Gaussian processes}},
  author = 	 {Titsias, Michalis},
  booktitle  = {Proc. AISTATS},
  year = 	 {2009},
  address = 	 {},
  month = 	 {},
  pdf = 	 {},
  url = 	 {},
  abstract = 	 {}
}
@inproceedings{Ginsbourger2021FastCO,
  title={Fast calculation of {G}aussian {P}rocess multiple-fold cross-validation residuals and their covariances},
  author={David Ginsbourger and Cedric Scharer},
  year={2021}
}

@misc{leclerc2022ffcv,
    author = {Guillaume Leclerc and Andrew Ilyas and Logan Engstrom and Sung Min Park and Hadi Salman and Aleksander Madry},
    title = {{FFCV}: Accelerating Training by Removing Data Bottlenecks},
    year = {2022},
    howpublished = {\url{https://github.com/libffcv/ffcv/}}
}