% Pre-training (Transfer Learning)
@inproceedings{hendrycks2019using,
  title={Using pre-training can improve model robustness and uncertainty},
  author={Hendrycks, Dan and Lee, Kimin and Mazeika, Mantas},
  booktitle={International conference on machine learning},
  pages={2712--2721},
  year={2019},
  organization={PMLR}
}

% fine-tuning intro1
@article{jiang2019smart,
  title={Smart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization},
  author={Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Zhao, Tuo},
  journal={arXiv preprint arXiv:1911.03437},
  year={2019}
}

% fine-tuning intro2
@article{choi2024autoft,
  title={AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data},
  author={Choi, Caroline and Lee, Yoonho and Chen, Annie and Zhou, Allan and Raghunathan, Aditi and Finn, Chelsea},
  journal={arXiv preprint arXiv:2401.10220},
  year={2024}
}

% BERT
@article{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}

@book{mackay2003information,
  title={Information theory, inference and learning algorithms},
  author={MacKay, David JC},
  year={2003},
  publisher={Cambridge university press}
}

% Natural Gradient with VI
@inproceedings{zhang2018noisy,
  title={Noisy natural gradient as variational inference},
  author={Zhang, Guodong and Sun, Shengyang and Duvenaud, David and Grosse, Roger},
  booktitle={International conference on machine learning},
  pages={5852--5861},
  year={2018},
  organization={PMLR}
}

% local entropy
@article{baldassi2015subdominant,
  title={Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses},
  author={Baldassi, Carlo and Ingrosso, Alessandro and Lucibello, Carlo and Saglietti, Luca and Zecchina, Riccardo},
  journal={Physical review letters},
  volume={115},
  number={12},
  pages={128101},
  year={2015},
  publisher={APS}
}

% Entropy sgd
@article{chaudhari2019entropy,
  title={Entropy-sgd: Biasing gradient descent into wide valleys},
  author={Chaudhari, Pratik and Choromanska, Anna and Soatto, Stefano and LeCun, Yann and Baldassi, Carlo and Borgs, Christian and Chayes, Jennifer and Sagun, Levent and Zecchina, Riccardo},
  journal={Journal of Statistical Mechanics: Theory and Experiment},
  volume={2019},
  number={12},
  pages={124018},
  year={2019},
  publisher={IOP Publishing}
}

% entropy sgld
@inproceedings{dziugaite2018entropy,
  title={Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors},
  author={Dziugaite, Gintare Karolina and Roy, Daniel},
  booktitle={International Conference on Machine Learning},
  pages={1377--1386},
  year={2018},
  organization={PMLR}
}

% BNN intro 1
@article{mackay1992practical,
  title={A practical Bayesian framework for backpropagation networks},
  author={MacKay, David JC},
  journal={Neural computation},
  volume={4},
  number={3},
  pages={448--472},
  year={1992},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

% BNN intro 2
@book{neal2012bayesian,
  title={Bayesian learning for neural networks},
  author={Neal, Radford M},
  volume={118},
  year={2012},
  publisher={Springer Science \& Business Media}
}

% BNN intro 3
@inproceedings{hinton1993keeping,
  title={Keeping the neural networks simple by minimizing the description length of the weights},
  author={Hinton, Geoffrey E and Van Camp, Drew},
  booktitle={Proceedings of the sixth annual conference on Computational learning theory},
  pages={5--13},
  year={1993}
}

% BNN uncertainty quantifiaction 1
@article{kapoor2022uncertainty,
  title={On uncertainty, tempering, and data augmentation in bayesian classification},
  author={Kapoor, Sanyam and Maddox, Wesley J and Izmailov, Pavel and Wilson, Andrew G},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={18211--18225},
  year={2022}
}

% BNN uncertainty quantifiaction 2
@inproceedings{kristiadi2022being,
  title={Being a bit frequentist improves Bayesian neural networks},
  author={Kristiadi, Agustinus and Hein, Matthias and Hennig, Philipp},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={529--545},
  year={2022},
  organization={PMLR}
}

% BNN BMA 1
@article{fragoso2018bayesian,
  title={Bayesian model averaging: A systematic review and conceptual classification},
  author={Fragoso, Tiago M and Bertoli, Wesley and Louzada, Francisco},
  journal={International Statistical Review},
  volume={86},
  number={1},
  pages={1--28},
  year={2018},
  publisher={Wiley Online Library}
}

% BNN BMA 2
@article{wilson2020bayesian,
  title={Bayesian deep learning and a probabilistic perspective of generalization},
  author={Wilson, Andrew G and Izmailov, Pavel},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={4697--4708},
  year={2020}
}

% BNN BMA 3
@article{zeng2024collapsed,
  title={Collapsed inference for bayesian deep learning},
  author={Zeng, Zhe and Van den Broeck, Guy},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

% Ensemble 1
@article{ganaie2022ensemble,
  title={Ensemble deep learning: A review},
  author={Ganaie, Mudasir A and Hu, Minghui and Malik, AK and Tanveer, M and Suganthan, PN},
  journal={Engineering Applications of Artificial Intelligence},
  volume={115},
  pages={105151},
  year={2022},
  publisher={Elsevier}
}

% Ensemble 2
@article{wild2024rigorous,
  title={A rigorous link between deep ensembles and (variational) bayesian methods},
  author={Wild, Veit David and Ghalebikesabi, Sahra and Sejdinovic, Dino and Knoblauch, Jeremias},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

% posterior quality 1
@article{kristiadi2022posterior,
  title={Posterior refinement improves sample efficiency in bayesian neural networks},
  author={Kristiadi, Agustinus and Eschenhagen, Runa and Hennig, Philipp},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={30333--30346},
  year={2022}
}

% posterior quality 2
@article{wenzel2020good,
  title={How good is the Bayes posterior in deep neural networks really?},
  author={Wenzel, Florian and Roth, Kevin and Veeling, Bastiaan S and {\'S}wi{\k{a}}tkowski, Jakub and Tran, Linh and Mandt, Stephan and Snoek, Jasper and Salimans, Tim and Jenatton, Rodolphe and Nowozin, Sebastian},
  journal={arXiv preprint arXiv:2002.02405},
  year={2020}
}

% MCMC 1 (SGLD)
@inproceedings{welling2011bayesian,
  title={Bayesian learning via stochastic gradient Langevin dynamics},
  author={Welling, Max and Teh, Yee W},
  booktitle={Proceedings of the 28th international conference on machine learning (ICML-11)},
  pages={681--688},
  year={2011}
}

% MCMC 2 (SGHMC)
@inproceedings{chen2014stochastic,
  title={Stochastic gradient hamiltonian monte carlo},
  author={Chen, Tianqi and Fox, Emily and Guestrin, Carlos},
  booktitle={International conference on machine learning},
  pages={1683--1691},
  year={2014},
  organization={PMLR}
}

% VI 1
@article{graves2011practical,
  title={Practical variational inference for neural networks},
  author={Graves, Alex},
  journal={Advances in neural information processing systems},
  volume={24},
  year={2011}
}

% VI 2
@inproceedings{blundell2015weight,
  title={Weight uncertainty in neural network},
  author={Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
  booktitle={International conference on machine learning},
  pages={1613--1622},
  year={2015},
  organization={PMLR}
}

% VI 3
@inproceedings{ranganath2014black,
  title={Black box variational inference},
  author={Ranganath, Rajesh and Gerrish, Sean and Blei, David},
  booktitle={Artificial intelligence and statistics},
  pages={814--822},
  year={2014},
  organization={PMLR}
}

% LA 1
@article{mackay1992bayesian,
  title={Bayesian interpolation},
  author={MacKay, David JC},
  journal={Neural computation},
  volume={4},
  number={3},
  pages={415--447},
  year={1992},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

% LA 2
@inproceedings{ritter2018scalable,
  title={A scalable laplace approximation for neural networks},
  author={Ritter, Hippolyt and Botev, Aleksandar and Barber, David},
  booktitle={6th International Conference on Learning Representations, ICLR 2018-Conference Track Proceedings},
  volume={6},
  year={2018},
  organization={International Conference on Representation Learning}
}

% LA 3 (Laplace redux)
@article{daxberger2021laplace,
  title={Laplace redux-effortless bayesian deep learning},
  author={Daxberger, Erik and Kristiadi, Agustinus and Immer, Alexander and Eschenhagen, Runa and Bauer, Matthias and Hennig, Philipp},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={20089--20103},
  year={2021}
}

% MCDropout
@inproceedings{gal2016dropout,
  title={Dropout as a bayesian approximation: Representing model uncertainty in deep learning},
  author={Gal, Yarin and Ghahramani, Zoubin},
  booktitle={international conference on machine learning},
  pages={1050--1059},
  year={2016},
  organization={PMLR}
}

% SWA
@article{izmailov2018averaging,
  title={Averaging weights leads to wider optima and better generalization},
  author={Izmailov, Pavel and Podoprikhin, Dmitrii and Garipov, Timur and Vetrov, Dmitry and Wilson, Andrew Gordon},
  journal={arXiv preprint arXiv:1803.05407},
  year={2018}
}

% SWAG
@article{maddox2019simple,
  title={A simple baseline for bayesian uncertainty in deep learning},
  author={Maddox, Wesley J and Izmailov, Pavel and Garipov, Timur and Vetrov, Dmitry P and Wilson, Andrew Gordon},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}


% Flat minima 1
@article{hochreiter1994simplifying,
  title={Simplifying neural nets by discovering flat minima},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Advances in neural information processing systems},
  volume={7},
  year={1994}
}

% Flat minima 2
@article{hochreiter1997flat,
  title={Flat minima},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={1},
  pages={1--42},
  year={1997},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}

% Flat minima 3 
@article{keskar2016large,
  title={On large-batch training for deep learning: Generalization gap and sharp minima},
  author={Keskar, Nitish Shirish and Mudigere, Dheevatsa and Nocedal, Jorge and Smelyanskiy, Mikhail and Tang, Ping Tak Peter},
  journal={arXiv preprint arXiv:1609.04836},
  year={2016}
}

% Flat minima 4
@article{neyshabur2017exploring,
  title={Exploring generalization in deep learning},
  author={Neyshabur, Behnam and Bhojanapalli, Srinadh and McAllester, David and Srebro, Nati},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{wortsman2022robust,
  title={Robust fine-tuning of zero-shot models},
  author={Wortsman, Mitchell and Ilharco, Gabriel and Kim, Jong Wook and Li, Mike and Kornblith, Simon and Roelofs, Rebecca and Lopes, Raphael Gontijo and Hajishirzi, Hannaneh and Farhadi, Ali and Namkoong, Hongseok and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7959--7971},
  year={2022}
}


% SAM
@article{foret2020sharpness,
  title={Sharpness-aware minimization for efficiently improving generalization},
  author={Foret, Pierre and Kleiner, Ariel and Mobahi, Hossein and Neyshabur, Behnam},
  journal={arXiv preprint arXiv:2010.01412},
  year={2020}
}

% FSAM
@inproceedings{kim2022fisher,
  title={Fisher sam: Information geometry and sharpness aware minimisation},
  author={Kim, Minyoung and Li, Da and Hu, Shell X and Hospedales, Timothy},
  booktitle={International Conference on Machine Learning},
  pages={11148--11161},
  year={2022},
  organization={PMLR}
}

% ASAM
@inproceedings{kwon2021asam,
  title={Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks},
  author={Kwon, Jungmin and Kim, Jeongseop and Park, Hyunseo and Choi, In Kwon},
  booktitle={International Conference on Machine Learning},
  pages={5905--5914},
  year={2021},
  organization={PMLR}
}

% ESAM
@article{du2021efficient,
  title={Efficient sharpness-aware minimization for improved training of neural networks},
  author={Du, Jiawei and Yan, Hanshu and Feng, Jiashi and Zhou, Joey Tianyi and Zhen, Liangli and Goh, Rick Siow Mong and Tan, Vincent YF},
  journal={arXiv preprint arXiv:2110.03141},
  year={2021}
}

% SAF
@article{du2022sharpness,
  title={Sharpness-Aware Training for Free},
  author={Du, Jiawei and Zhou, Daquan and Feng, Jiashi and Tan, Vincent YF and Zhou, Joey Tianyi},
  journal={arXiv preprint arXiv:2205.14083},
  year={2022}
}

% LookSAM
@inproceedings{liu2022towards,
  title={Towards efficient and scalable sharpness-aware minimization},
  author={Liu, Yong and Mai, Siqi and Chen, Xiangning and Hsieh, Cho-Jui and You, Yang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12360--12370},
  year={2022}
}

% SS-SAM
@article{mi2022make,
  title={Make sharpness-aware minimization stronger: A sparsified perturbation approach},
  author={Mi, Peng and Shen, Li and Ren, Tianhe and Zhou, Yiyi and Sun, Xiaoshuai and Ji, Rongrong and Tao, Dacheng},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={30950--30962},
  year={2022}
}

% SAM-ON
@article{mueller2024normalization,
  title={Normalization layers are all that sharpness-aware minimization needs},
  author={Mueller, Maximilian and Vlaar, Tiffany and Rolnick, David and Hein, Matthias},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

% partial fine-tuning
@inproceedings{shen2021partial,
  title={Partial is better than all: revisiting fine-tuning strategy for few-shot learning},
  author={Shen, Zhiqiang and Liu, Zechun and Qin, Jie and Savvides, Marios and Cheng, Kwang-Ting},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={11},
  pages={9594--9602},
  year={2021}
}

% bSAM
@article{mollenhoff2022sam,
  title={SAM as an Optimal Relaxation of Bayes},
  author={M{\"o}llenhoff, Thomas and Khan, Mohammad Emtiyaz},
  journal={arXiv preprint arXiv:2210.01620},
  year={2022}
}

% Adam-like VI
@inproceedings{khan2018fast,
  title={Fast and scalable bayesian deep learning by weight-perturbation in adam},
  author={Khan, Mohammad and Nielsen, Didrik and Tangkaratt, Voot and Lin, Wu and Gal, Yarin and Srivastava, Akash},
  booktitle={International conference on machine learning},
  pages={2611--2620},
  year={2018},
  organization={PMLR}
}

% Random SAM, Variational SAM
@article{ujvary2022rethinking,
  title={Rethinking Sharpness-Aware Minimization as Variational Inference},
  author={Ujv{\'a}ry, Szilvia and Telek, Zsigmond and Kerekes, Anna and M{\'e}sz{\'a}ros, Anna and Husz{\'a}r, Ferenc},
  journal={arXiv preprint arXiv:2210.10452},
  year={2022}
}


% Pre-Train Your Loss
@article{shwartz2022pre,
  title={Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors},
  author={Shwartz-Ziv, Ravid and Goldblum, Micah and Souri, Hossein and Kapoor, Sanyam and Zhu, Chen and LeCun, Yann and Wilson, Andrew Gordon},
  journal={arXiv preprint arXiv:2205.10279},
  year={2022}
}

% Laplace Redux
@inproceedings{laplace2021,
  title={Laplace Redux--Effortless {B}ayesian Deep Learning},
  author={Erik Daxberger and Agustinus Kristiadi and Alexander Immer 
          and Runa Eschenhagen and Matthias Bauer and Philipp Hennig},
  booktitle={{N}eur{IPS}},
  year={2021}
}

% MOPED
@inproceedings{krishnan2020specifying,
  title={Specifying weight priors in bayesian deep neural networks with empirical bayes},
  author={Krishnan, Ranganath and Subedar, Mahesh and Tickoo, Omesh},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={4477--4484},
  year={2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5875}
}

% Bayesian-torch
@software{krishnan2022bayesiantorch,
  author       = {Ranganath Krishnan and Pi Esposito and Mahesh Subedar},               
  title        = {Bayesian-Torch: Bayesian neural network layers for uncertainty estimation},
  month        = jan,
  year         = 2022,
  doi          = {10.5281/zenodo.5908307},
  url          = {https://doi.org/10.5281/zenodo.5908307},
  howpublished = {\url{https://github.com/IntelLabs/bayesian-torch}}
}


% BayesAdapter
@article{deng2020bayesadapter,
  title={Bayesadapter: Being bayesian, inexpensively and reliably, via bayesian fine-tuning},
  author={Deng, Zhijie and Zhang, Hao and Yang, Xiao and Dong, Yinpeng and Zhu, Jun},
  journal={arXiv preprint arXiv:2010.01979},
  year={2020}
}

% F-SWAG
@article{nguyen2023flat,
  title={Flat Seeking Bayesian Neural Networks},
  author={Nguyen, Van-Anh and Vuong, Tung-Long and Phan, Hoang and Do, Thanh-Toan and Phung, Dinh and Le, Trung},
  journal={arXiv preprint arXiv:2302.02713},
  year={2023}
}


% BLL
@inproceedings{harrison2023variational,
  title={Variational Bayesian Last Layers},
  author={Harrison, James and Willes, John and Snoek, Jasper},
  booktitle={Fifth Symposium on Advances in Approximate Bayesian Inference},
  year={2023}
}

% First-layer BNNs
@inproceedings{sharma2023bayesian,
  title={Do Bayesian Neural Networks Need To Be Fully Stochastic?},
  author={Sharma, Mrinank and Farquhar, Sebastian and Nalisnick, Eric and Rainforth, Tom},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={7694--7722},
  year={2023},
  organization={PMLR}
}

% rank-1 parameterization of BNNs
@inproceedings{dusenberry2020efficient,
  title={Efficient and scalable bayesian neural nets with rank-1 factors},
  author={Dusenberry, Michael and Jerfel, Ghassen and Wen, Yeming and Ma, Yian and Snoek, Jasper and Heller, Katherine and Lakshminarayanan, Balaji and Tran, Dustin},
  booktitle={International conference on machine learning},
  pages={2782--2792},
  year={2020},
  organization={PMLR}
}

% subnetwork laplace
@inproceedings{daxberger2021bayesian,
  title={Bayesian deep learning via subnetwork inference},
  author={Daxberger, Erik and Nalisnick, Eric and Allingham, James U and Antor{\'a}n, Javier and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
  booktitle={International Conference on Machine Learning},
  pages={2510--2521},
  year={2021},
  organization={PMLR}
}

% subspace BNN
@inproceedings{izmailov2020subspace,
  title={Subspace inference for Bayesian deep learning},
  author={Izmailov, Pavel and Maddox, Wesley J and Kirichenko, Polina and Garipov, Timur and Vetrov, Dmitry and Wilson, Andrew Gordon},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={1169--1179},
  year={2020},
  organization={PMLR}
}

@inproceedings{snoek2015scalable,
  title={Scalable bayesian optimization using deep neural networks},
  author={Snoek, Jasper and Rippel, Oren and Swersky, Kevin and Kiros, Ryan and Satish, Nadathur and Sundaram, Narayanan and Patwary, Mostofa and Prabhat, Mr and Adams, Ryan},
  booktitle={International conference on machine learning},
  pages={2171--2180},
  year={2015},
  organization={PMLR}
}

% local entropy 2
@article{baldassi2016unreasonable,
  title={Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes},
  author={Baldassi, Carlo and Borgs, Christian and Chayes, Jennifer T and Ingrosso, Alessandro and Lucibello, Carlo and Saglietti, Luca and Zecchina, Riccardo},
  journal={Proceedings of the National Academy of Sciences},
  volume={113},
  number={48},
  pages={E7655--E7662},
  year={2016},
  publisher={National Acad Sciences}
}

% Resnet
@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

% Batch Normalization
@inproceedings{ioffe2015batch,
  title={Batch normalization: Accelerating deep network training by reducing internal covariate shift},
  author={Ioffe, Sergey and Szegedy, Christian},
  booktitle={International conference on machine learning},
  pages={448--456},
  year={2015},
  organization={pmlr}
}

% Make Me a BNN
@article{franchi2023make,
  title={Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models},
  author={Franchi, Gianni and Laurent, Olivier and Legu{\'e}ry, Maxence and Bursuc, Andrei and Pilzer, Andrea and Yao, Angela},
  journal={arXiv preprint arXiv:2312.15297},
  year={2023}
}

% recent research of transfer learning
@article{neyshabur2020being,
  title={What is being transferred in transfer learning?},
  author={Neyshabur, Behnam and Sedghi, Hanie and Zhang, Chiyuan},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={512--523},
  year={2020}
}

% benefit of transfer learning
@article{iman2023review,
  title={A review of deep transfer learning and recent advancements},
  author={Iman, Mohammadreza and Arabnia, Hamid Reza and Rasheed, Khaled},
  journal={Technologies},
  volume={11},
  number={2},
  pages={40},
  year={2023},
  publisher={MDPI}
}

% benefit of transfer learning
@article{zhuang2020comprehensive,
  title={A comprehensive survey on transfer learning},
  author={Zhuang, Fuzhen and Qi, Zhiyuan and Duan, Keyu and Xi, Dongbo and Zhu, Yongchun and Zhu, Hengshu and Xiong, Hui and He, Qing},
  journal={Proceedings of the IEEE},
  volume={109},
  number={1},
  pages={43--76},
  year={2020},
  publisher={IEEE}
}

% benefit of transfer learning
@article{farahani2021brief,
  title={A brief review of domain adaptation},
  author={Farahani, Abolfazl and Voghoei, Sahar and Rasheed, Khaled and Arabnia, Hamid R},
  journal={Advances in data science and information engineering: proceedings from ICDATA 2020 and IKE 2020},
  pages={877--894},
  year={2021},
  publisher={Springer}
}

% fewshot overfit
@article{lu2022makes,
  title={What Makes Pre-trained Language Models Better Zero/Few-shot Learners?},
  author={Lu, Jinghui and Zhao, Rui and Mac Namee, Brian and Zhu, Dongsheng and Han, Weidong and Tan, Fei},
  journal={arXiv preprint arXiv:2209.15206},
  year={2022}
}

% BNN appliance auto drive
@inproceedings{michelmore2020uncertainty,
  title={Uncertainty quantification with statistical guarantees in end-to-end autonomous driving control},
  author={Michelmore, Rhiannon and Wicker, Matthew and Laurenti, Luca and Cardelli, Luca and Gal, Yarin and Kwiatkowska, Marta},
  booktitle={2020 IEEE international conference on robotics and automation (ICRA)},
  pages={7344--7350},
  year={2020},
  organization={IEEE}
}

%EMCMC
@inproceedings{li2023entropy,
  title={Entropy-MCMC: Sampling from Flat Basins with Ease},
  author={Li, Bolian and Zhang, Ruqi},
  booktitle={NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations},
  year={2023}
}


%bnn survey
@article{goan2020bayesian,
  title={Bayesian neural networks: An introduction and survey},
  author={Goan, Ethan and Fookes, Clinton},
  journal={Case Studies in Applied Bayesian Data Science: CIRM Jean-Morlet Chair, Fall 2018},
  pages={45--87},
  year={2020},
  publisher={Springer}
}

%bma
@article{wasserman2000bayesian,
  title={Bayesian model selection and model averaging},
  author={Wasserman, Larry},
  journal={Journal of mathematical psychology},
  volume={44},
  number={1},
  pages={92--107},
  year={2000},
  publisher={Elsevier}
}

%sharp1
@inproceedings{dinh2017sharp,
  title={Sharp minima can generalize for deep nets},
  author={Dinh, Laurent and Pascanu, Razvan and Bengio, Samy and Bengio, Yoshua},
  booktitle={International Conference on Machine Learning},
  pages={1019--1028},
  year={2017},
  organization={PMLR}
}

%sharp2
@inproceedings{kaur2023maximum,
  title={On the maximum hessian eigenvalue and generalization},
  author={Kaur, Simran and Cohen, Jeremy and Lipton, Zachary Chase},
  booktitle={Proceedings on},
  pages={51--65},
  year={2023},
  organization={PMLR}
}

%sharp3
@inproceedings{ghorbani2019investigation,
  title={An investigation into neural net optimization via hessian eigenvalue density},
  author={Ghorbani, Behrooz and Krishnan, Shankar and Xiao, Ying},
  booktitle={International Conference on Machine Learning},
  pages={2232--2241},
  year={2019},
  organization={PMLR}
}

%sharp4
@inproceedings{sato2022pof,
  title={Pof: Post-training of feature extractor for improving generalization},
  author={Sato, Ikuro and Ryota, Yamada and Tanaka, Masayuki and Inoue, Nakamasa and Kawakami, Rei},
  booktitle={International Conference on Machine Learning},
  pages={19221--19230},
  year={2022},
  organization={PMLR}
}

% Weyl's Inequality
@article{weyl1912asymptotische,
  title={Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung)},
  author={Weyl, Hermann},
  journal={Mathematische Annalen},
  volume={71},
  number={4},
  pages={441--479},
  year={1912},
  publisher={Springer}
}

%vit
@article{dosovitskiy2020image,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

%eigen value ratio
@article{li2018visualizing,
  title={Visualizing the loss landscape of neural nets},
  author={Li, Hao and Xu, Zheng and Taylor, Gavin and Studer, Christoph and Goldstein, Tom},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

%cifar10-c
@article{hendrycks2019benchmarking,
  title={Benchmarking neural network robustness to common corruptions and perturbations},
  author={Hendrycks, Dan and Dietterich, Thomas},
  journal={arXiv preprint arXiv:1903.12261},
  year={2019}
}

@inproceedings{kumar2022fine,
  title={Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution},
  author={Kumar, Ananya and Raghunathan, Aditi and Jones, Robbie and Ma, Tengyu and Liang, Percy},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

% posterior
@inproceedings{izmailov2021bayesian,
  title={What are Bayesian neural network posteriors really like?},
  author={Izmailov, Pavel and Vikram, Sharad and Hoffman, Matthew D and Wilson, Andrew Gordon Gordon},
  booktitle={International conference on machine learning},
  pages={4629--4640},
  year={2021},
  organization={PMLR}
}


% training batchnrom only
@article{frankle2020training,
  title={Training batchnorm and only batchnorm: On the expressive power of random features in cnns},
  author={Frankle, Jonathan and Schwab, David J and Morcos, Ari S},
  journal={arXiv preprint arXiv:2003.00152},
  year={2020}
}

% pseudo inverse matrix 1
@article{gentle2007matrix,
  title={Matrix algebra},
  author={Gentle, James E},
  journal={Springer texts in statistics, Springer, New York, NY, doi},
  volume={10},
  pages={978--0},
  year={2007},
  publisher={Springer}
}

% pseudo inverse matrix 2
@book{sidi2017vector,
  title={Vector extrapolation methods with applications},
  author={Sidi, Avram},
  year={2017},
  publisher={SIAM}
}

% pseudo inverse matrix 3
@article{wynn1962acceleration,
  title={Acceleration techniques for iterated vector and matrix problems},
  author={Wynn, Peter},
  journal={Mathematics of Computation},
  volume={16},
  number={79},
  pages={301--322},
  year={1962}
}

% cifar10/100
@article{krizhevsky2009learning,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Hinton, Geoffrey and others},
  year={2009},
  publisher={Toronto, ON, Canada}
}

& ImageNet
@article{russakovsky2015imagenet,
  title={Imagenet large scale visual recognition challenge},
  author={Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and others},
  journal={International journal of computer vision},
  volume={115},
  pages={211--252},
  year={2015},
  publisher={Springer}
}

@inproceedings{springersharpness,
  title={Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning},
  author={Springer, Jacob Mitchell and Nagarajan, Vaishnavh and Raghunathan, Aditi},
  booktitle={The Twelfth International Conference on Learning Representations}
}

% CLIP
@inproceedings{radford2021learning,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PMLR}
}

%% Few-shot CLIP protocol
@article{zhu2023enhancing,
  title={Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning},
  author={Zhu, Yao and Chen, Yuefeng and Wang, Wei and Mao, Xiaofeng and Wang, Yue and Li, Zhigang and Wang, Jindong and Ji, Xiangyang and others},
  journal={arXiv preprint arXiv:2311.04464},
  year={2023}
}

%% Few-shot CLIP protocol 2
@inproceedings{lin2023multimodality,
  title={Multimodality helps unimodality: Cross-modal few-shot learning with multimodal models},
  author={Lin, Zhiqiu and Yu, Samuel and Kuang, Zhiyi and Pathak, Deepak and Ramanan, Deva},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19325--19337},
  year={2023}
}



% IN-V2
@inproceedings{recht2019imagenet,
  title={Do imagenet classifiers generalize to imagenet?},
  author={Recht, Benjamin and Roelofs, Rebecca and Schmidt, Ludwig and Shankar, Vaishaal},
  booktitle={International conference on machine learning},
  pages={5389--5400},
  year={2019},
  organization={PMLR}
}

% IN-R
@inproceedings{hendrycks2021many,
  title={The many faces of robustness: A critical analysis of out-of-distribution generalization},
  author={Hendrycks, Dan and Basart, Steven and Mu, Norman and Kadavath, Saurav and Wang, Frank and Dorundo, Evan and Desai, Rahul and Zhu, Tyler and Parajuli, Samyak and Guo, Mike and others},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={8340--8349},
  year={2021}
}

& IN-A
@inproceedings{hendrycks2021natural,
  title={Natural adversarial examples},
  author={Hendrycks, Dan and Zhao, Kevin and Basart, Steven and Steinhardt, Jacob and Song, Dawn},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={15262--15271},
  year={2021}
}

% IN-S
@article{wang2019learning,
  title={Learning robust global representations by penalizing local predictive power},
  author={Wang, Haohan and Ge, Songwei and Lipton, Zachary and Xing, Eric P},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}


@article{lee2024enhancing,
  title={Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling},
  author={Lee, Hyungi and Nam, Giung and Fong, Edwin and Lee, Juho},
  journal={arXiv preprint arXiv:2403.07282},
  year={2024}
}

@article{jastrzebski2020break,
  title={The break-even point on optimization trajectories of deep neural networks},
  author={Jastrzebski, Stanislaw and Szymczak, Maciej and Fort, Stanislav and Arpit, Devansh and Tabor, Jacek and Cho, Kyunghyun and Geras, Krzysztof},
  journal={arXiv preprint arXiv:2002.09572},
  year={2020}
}

% ECE
@inproceedings{guo2017calibration,
  title={On calibration of modern neural networks},
  author={Guo, Chuan and Pleiss, Geoff and Sun, Yu and Weinberger, Kilian Q},
  booktitle={International conference on machine learning},
  pages={1321--1330},
  year={2017},
  organization={PMLR}
}

% EuroSAT
@article{helber2019eurosat,
  title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
  author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume={12},
  number={7},
  pages={2217--2226},
  year={2019},
  publisher={IEEE}
}

% Oxford Flowers
@inproceedings{nilsback2008automated,
  title={Automated flower classification over a large number of classes},
  author={Nilsback, Maria-Elena and Zisserman, Andrew},
  booktitle={2008 Sixth Indian conference on computer vision, graphics \& image processing},
  pages={722--729},
  year={2008},
  organization={IEEE}
}

% Oxford Pets
@inproceedings{parkhi2012cats,
  title={Cats and dogs},
  author={Parkhi, Omkar M and Vedaldi, Andrea and Zisserman, Andrew and Jawahar, CV},
  booktitle={2012 IEEE conference on computer vision and pattern recognition},
  pages={3498--3505},
  year={2012},
  organization={IEEE}
}

% UCF101
@article{soomro2012ucf101,
  title={UCF101: A dataset of 101 human actions classes from videos in the wild},
  author={Soomro, Khurram and Zamir, Amir Roshan and Shah, Mubarak},
  journal={arXiv preprint arXiv:1212.0402},
  year={2012}
}

@article{marek2024can,
  title={Can a Confident Prior Replace a Cold Posterior?},
  author={Marek, Martin and Paige, Brooks and Izmailov, Pavel},
  journal={arXiv preprint arXiv:2403.01272},
  year={2024}
}

% ViT small-scale dataset 1
@article{liu2021efficient,
  title={Efficient training of visual transformers with small datasets},
  author={Liu, Yahui and Sangineto, Enver and Bi, Wei and Sebe, Nicu and Lepri, Bruno and Nadai, Marco},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={23818--23830},
  year={2021}
}

% ViT small-scale dataset 2
@article{zhu2023understanding,
  title={Understanding why ViT trains badly on small datasets: an intuitive perspective},
  author={Zhu, Haoran and Chen, Boyuan and Yang, Carter},
  journal={arXiv preprint arXiv:2302.03751},
  year={2023}
}

% natural gradient
@article{amari1998natural,
  title={Natural gradient works efficiently in learning},
  author={Amari, Shun-Ichi},
  journal={Neural computation},
  volume={10},
  number={2},
  pages={251--276},
  year={1998},
  publisher={MIT Press}
}

% DiWA
@article{rame2022diverse,
  title={Diverse weight averaging for out-of-distribution generalization},
  author={Rame, Alexandre and Kirchmeyer, Matthieu and Rahier, Thibaud and Rakotomamonjy, Alain and Gallinari, Patrick and Cord, Matthieu},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={10821--10836},
  year={2022}
}

% Model Soup
@inproceedings{wortsman2022model,
  title={Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time},
  author={Wortsman, Mitchell and Ilharco, Gabriel and Gadre, Samir Ya and Roelofs, Rebecca and Gontijo-Lopes, Raphael and Morcos, Ari S and Namkoong, Hongseok and Farhadi, Ali and Carmon, Yair and Kornblith, Simon and others},
  booktitle={International conference on machine learning},
  pages={23965--23998},
  year={2022},
  organization={PMLR}
}


% LeNet
@article{lecun1998gradient,
  title={Gradient-based learning applied to document recognition},
  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}
}

% Model Misspecification
@article{muller2013risk,
  title={Risk of Bayesian inference in misspecified models, and the sandwich covariance matrix},
  author={M{\"u}ller, Ulrich K},
  journal={Econometrica},
  volume={81},
  number={5},
  pages={1805--1849},
  year={2013},
  publisher={Wiley Online Library}
}

@article{izmailov2021dangers,
  title={Dangers of Bayesian model averaging under covariate shift},
  author={Izmailov, Pavel and Nicholson, Patrick and Lotfi, Sanae and Wilson, Andrew G},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={3309--3322},
  year={2021}
}


@article{kim2022sufficient,
  title={Sufficient invariant learning for distribution shift},
  author={Kim, Taero and Park, Subeen and Lim, Sungjun and Jung, Yonghan and Muandet, Krikamol and Song, Kyungwoo},
  journal={arXiv preprint arXiv:2210.13533},
  year={2022}
}

@inproceedings{zhang2023flatness,
  title={Flatness-aware minimization for domain generalization},
  author={Zhang, Xingxuan and Xu, Renzhe and Yu, Han and Dong, Yancheng and Tian, Pengfei and Cui, Peng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={5189--5202},
  year={2023}
}

% Eigen-SAM
@article{luo2024explicit,
  title={Explicit eigenvalue regularization improves sharpness-aware minimization},
  author={Luo, Haocheng and Truong, Tuan and Pham, Tung and Harandi, Mehrtash and Phung, Dinh and Le, Trung},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={4424--4453},
  year={2024}
}

% MoLA
@article{eschenhagen2021mixtures,
  title={Mixtures of laplace approximations for improved post-hoc uncertainty in deep learning},
  author={Eschenhagen, Runa and Daxberger, Erik and Hennig, Philipp and Kristiadi, Agustinus},
  journal={arXiv preprint arXiv:2111.03577},
  year={2021}
}

% IVON
@article{shen2024variational,
  title={Variational learning is effective for large deep networks},
  author={Shen, Yuesong and Daheim, Nico and Cong, Bai and Nickl, Peter and Marconi, Gian Maria and Bazan, Clement and Yokota, Rio and Gurevych, Iryna and Cremers, Daniel and Khan, Mohammad Emtiyaz and others},
  journal={arXiv preprint arXiv:2402.17641},
  year={2024}
}


@book{bishop2006pattern,
  title={Pattern recognition and machine learning},
  author={Bishop, Christopher M and Nasrabadi, Nasser M},
  volume={4},
  number={4},
  year={2006},
  publisher={Springer}
}

@book{murphy2012machine,
  title={Machine learning: a probabilistic perspective},
  author={Murphy, Kevin P},
  year={2012},
  publisher={MIT press}
}

@book{gelman1995bayesian,
  title={Bayesian data analysis},
  author={Gelman, Andrew and Carlin, John B and Stern, Hal S and Rubin, Donald B},
  year={1995},
  publisher={Chapman and Hall/CRC}
}

@inproceedings{lawrence2004learning,
  title={Learning to learn with the informative vector machine},
  author={Lawrence, Neil D and Platt, John C},
  booktitle={Proceedings of the twenty-first international conference on Machine learning},
  pages={65},
  year={2004}
}

@inproceedings{raina2006constructing,
  title={Constructing informative priors using transfer learning},
  author={Raina, Rajat and Ng, Andrew Y and Koller, Daphne},
  booktitle={Proceedings of the 23rd international conference on Machine learning},
  pages={713--720},
  year={2006}
}