[BENW15] Rafael Barbosa, Alina Ene, Huy Nguyen, and Justin Ward. The power of randomization: Distributed submodular maximization on massive datasets. In International Conference on Machine Learning, pages 1236-1244, 2015.
[BQK+14] Nematollah Kayhan Batmanghelich, Gerald Quon, Alex Kulesza, Manolis Kellis, Polina Golland, and Luke Bornn. Diversifying sparsity using variational determinantal point processes. arXiv preprint arXiv:1411.6307, 2014.
[CGGS15] Wei-Lun Chao, Boqing Gong, Kristen Grauman, and Fei Sha. Large-margin determinantal point processes. In UAI, pages 191-200, 2015.
[ÇMI09] Ali Çivril and Malik Magdon-Ismail. On selecting a maximum volume sub-matrix of a matrix and related problems. Theoretical Computer Science, 410(47-49):4801-4811, 2009.
[CMI13] Ali Civil and Malik Magdon-Ismail. Exponential inapproximability of selecting a maximum volume sub-matrix. Algorithmica, 65(1):159-176, 2013.
[DZ07] Jiu Ding and Aihui Zhou. Eigenvalues of rank-one updated matrices with some applications. Applied Mathematics Letters, 20(12):1223-1226, 2007.
[GCGS14] Boqing Gong, Wei-Lun Chao, Kristen Grauman, and Fei Sha. Diverse sequential subset selection for supervised video summarization. In Advances in Neural Information Processing Systems, pages 2069–2077, 2014.
[IMGR20] Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, and Alireza Rezaei. Composable core-sets for determinant maximization problems via spectral spanners. In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 1675–1694. SIAM, 2020.
[IMMM14] Piotr Indyk, Sepideh Mahabadi, Mohammad Mahdian, and Vahab S Mirrokni. Composable core-sets for diversity and coverage maximization. In Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 100–108. ACM, 2014.
[KT11] Alex Kulesza and Ben Taskar. Learning determinantal point processes. 2011.
$\left[\mathrm{KT}^{+}12\right]$  Alex Kulesza, Ben Taskar, et al. Determinantal point processes for machine learning. Foundations and Trends® in Machine Learning, 5(2-3):123-286, 2012.
[LLBBH98] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[LCYO16] Donghoon Lee, Geonho Cha, Ming-Hsuan Yang, and Songhwai Oh. Individualness and determinantal point processes for pedestrian detection. In European Conference on Computer Vision, pages 330-346. Springer, 2016.
[LJS16] Chengtao Li, Stefanie Jegelka, and Suvrit Sra. Efficient sampling for k-determinantal point processes. In Artificial Intelligence and Statistics, pages 1328-1337. PMLR, 2016.
[MIGR19] Sepideh Mahabadi, Piotr Indyk, Shayan Oveis Gharan, and Alireza Rezaei. Composable core-sets for determinant maximization: A simple near-optimal algorithm. In International Conference on Machine Learning, pages 4254–4263. PMLR, 2019.
[MJK17] Baharan Mirzasoleiman, Stefanie Jegelka, and Andreas Krause. Streaming non-monotone submodular maximization: Personalized video summarization on the fly. arXiv preprint arXiv:1706.03583, 2017.
[MKBK15] Baharan Mirzasoleiman, Amin Karbasi, Ashwinkumar Badanidiyuru, and Andreas Krause. Distributed submodular cover: Succinctly summarizing massive data. In Advances in Neural Information Processing Systems, pages 2881-2889, 2015.