[86] Max-Heinrich Laves, Sontje Ihler, Karl-Philipp Kortmann, and Tobias Ortmaier. Calibration of model uncertainty for dropout variational inference. arXiv preprint arXiv:2006.11584, 2020.
[87] Shoushan Li and Chengqing Zong. Multi-domain sentiment classification. In Proceedings of ACL-08: HLT, short papers, pages 257–260, 2008.
[88] Yongxin Yang and Timothy M Hospedales. A unified perspective on Multi-Domain and Multi-Task learning. arXiv preprint arXiv:1412.7489, 2014.
[89] Hakan Bilen and Andrea Vedaldi. Universal representations: the missing link between faces, text, planktons, and cat breeds. arXiv preprint arXiv:1701.07275, 2017.
[90] Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni. Federated learning with matched averaging. arXiv preprint arXiv:2002.06440, 2020.
[91] In S Jeon, Deokyoung Kang, and Suk I Yoo. Blind image deconvolution using student's-t prior with overlapping group sparsity. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1817-1821. IEEE, March 2017.
[92] Christopher M Bishop and Nasser M Nasrabadi. Pattern Recognition and Machine Learning, volume 4. Springer, 2006.
[93] David JC MacKay. A practical bayesian framework for backpropagation networks. Neural computation, 4(3):448-472, 1992.
[94] David Barber and Christopher M Bishop. Ensemble learning in bayesian neural networks. *Nato ASI Series F Computer and Systems Sciences*, 168:215–238, 1998.
[95] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting, 2014.
[96] Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
[97] Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus. Regularization of neural networks using DropConnect. In Sanjoy Dasgupta and David McAllester, editors, Proceedings of the 30th International Conference on Machine Learning, volume 28 of Proceedings of Machine Learning Research, pages 1058-1066, Atlanta, Georgia, USA, 2013. PMLR.
[98] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images, 2009.
[99] Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik. EMNIST: Extending MNIST to handwritten letters. In 2017 international joint conference on neural networks (IJCNN), pages 2921-2926. IEEE, 2017.
[100] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 3730-3738. IEEE, December 2015.
[101] Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr. SNIP: Single-shot network pruning based on connection sensitivity. arXiv preprint arXiv:1810.02340, 2018.
[102] Yuang Jiang, Shiqiang Wang, Victor Valls, Bong Jun Ko, Wei-Han Lee, Kin K Leung, and Leandros Tassiulas. Model pruning enables efficient federated learning on edge devices, 2022.
[103] Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, pages 2623-2631, New York, NY, USA, July 2019. Association for Computing Machinery.
[104] Zachary Lipton, Yu-Xiang Wang, and Alexander Smola. Detecting and correcting for label shift with black box predictors. In International conference on machine learning, pages 3122-3130. PMLR, 2018.
[105] Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick, Grigorios Chrysos, and Volkan Cevher. Federated learning under covariate shifts with generalization guarantees. arXiv preprint arXiv:2306.05325, 2023.