1. Askari, A., Yang, F., El Ghaoui, L. (2018). Kernel-based outlier detection using the inverse Christoffel function. arxiv preprint arxiv:1806.06775
2. Bach, F. (2013). Sharp analysis of low-rank kernel matrix approximations. In: Proceedings of the COLT Conference on Learning Theory, pp. 185–209
3. Belabbas, M. A., & Wolfe, P. J. (2009). Spectral methods in machine learning and new strategies for very large datasets. Proceedings of the National Academy of Sciences, 106(2), 369–374.
4. Binev, P., Cohen, A., Dahmen, W., DeVore, R., Petrova, G., & Wojtaszczyk, P. (2011). Convergence rates for greedy algorithms in reduced basis methods. SIAM Journal on Mathematical Analysis, 43(3), 1457–1472.
5. Calandriello, D., Lazaric, A., & Valko, M. (2017a). Distributed adaptive sampling for kernel matrix approximation. In: Singh A, Zhu XJ (eds) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20–22 April 2017, Fort Lauderdale, FL, USA, PMLR, Proceedings of Machine Learning Research, vol. 54, pp. 1421–1429.