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Selecting diverse and important items, called landmarks, from a large set is a problem of interest in machine learning.
Pinkus A (1979) Matrices and n n -widths. Linear Algebra and Applications 27:245–278
1979
Earlier work this paper cites.
Corless RM, Gonnet GH, Hare DEG, Jeffrey DJ, Knuth DE (1996) On the Lambert W Function. In: Advances in computational mathematics, pp 329–359
1996
Earlier work this paper cites.
Guyon I, Matic N, Vapnik V (1996) Advances in Knowledge Discovery and Data Mining, chap Discovering Informative Patterns and Data Cleaning, pp 181–203
1996
Earlier work this paper cites.
Williams C, Seeger M (2001) Using the Nyström method to speed up kernel machines. In: Advances in Neural Information Processing Systems 13, pp 682–688
2001
Earlier work this paper cites.
2002
Earlier work this paper cites.
Girolami M (2002) Orthogonal series density estimation and the kernel eigenvalue problem. Neural Comput 14(3):669–688
2002
Earlier work this paper cites.
Suykens JAK, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Least Squares Support Vector Machines. World Scientific, Singapore
2002
Earlier work this paper cites.
Drineas P, Mahoney MW (2005) On the Nyström method for approximating a gram matrix for improved kernel-based learning. J Mach Learn Res 6:2153–2175
2005
Earlier work this paper cites.
Hough JB, Krishnapur M, Peres Y, Virág B (2006) Determinantal processes and independence. Probab Surveys 3:206–229
2006
Earlier work this paper cites.
Rasmussen C, Williams C (2006) Gaussian Processes for Machine Learning. The MIT Press, Cambridge
2006
Earlier work this paper cites.
Rahimi A, Recht B (2007) Random features for large-scale kernel machines. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, pp 1177–1184
2007
Earlier work this paper cites.
Belabbas MA, Wolfe PJ (2009) Spectral methods in machine learning and new strategies for very large datasets. Proceedings of the National Academy of Sciences 106(2):369–374
2009
Earlier work this paper cites.
Cortes C, Mohri M, Talwalkar A (2010) On the impact of kernel approximation on learning accuracy. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp 113–120
2010
Earlier work this paper cites.
Kulesza A, Taskar B (2010) Structured determinantal point processes. In: Advances in neural information processing systems, pp 1171–1179
2010
Earlier work this paper cites.
Paisley J, Liao X, Carin L (2010) Active learning and basis selection for kernel-based linear models: A bayesian perspective. IEEE Transactions on Signal Processing 58(5):2686–2700
2010
Earlier work this paper cites.
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
2011
Earlier work this paper cites.
Farahat A, Ghodsi A, Kamel M (2011) A novel greedy algorithm for Nyström approximation. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol 15, pp 269–277
2011
Earlier work this paper cites.
Kulesza A, Taskar B (2011) k-dpps: Fixed-size determinantal point processes. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp 1193–1200
2011
Earlier work this paper cites.
Tropp J (2011) Freedman’s inequality for matrix martingales. Electron Commun Probab 16:262–270
2011
Cited alongside, same era.
Bach F (2013) Sharp analysis of low-rank kernel matrix approximations. In: COLT Conference on Learning Theory, pp 185–209
2013
Cited alongside, same era.
DeVore R, Petrova G, Wojtaszczyk P (2013) Greedy algorithms for reduced bases in Banach spaces. Constructive Approximation 37(3):455–466
2013
Cited alongside, same era.
Papailiopoulos D, Kyrillidis A, Boutsidis C (2014) Provable deterministic leverage score sampling. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, KDD ’14, pp 997–1006
2014
Cited alongside, same era.
Valverde-Albacete FJ, Peláez-Moreno C (2014) 100% classification accuracy considered harmful: The normalized information transfer factor explains the accuracy paradox. PloS one 9(1):e84217
Pauwels E, Bach F, Vert J (2018) Relating Leverage Scores and Density using Regularized Christoffel Functions. In: Advances in Neural Information Processing Systems 32, pp 1663–1672
2018
Later among the works it cites.
Rudi A, Calandriello D, Carratino L, Rosasco L (2018) On fast leverage score sampling and optimal learning. In: Advances in Neural Information Processing Systems 31, pp 5677–5687
2018
Later among the works it cites.
Chen V, Wu S, Ratner AJ, Weng J, Ré C (2019) Slice-based learning: A programming model for residual learning in critical data slices. In: Advances in neural information processing systems, pp 9392–9402
2019
Closest in time.
Derezinski M, Calandriello D, Valko M (2019) Exact sampling of determinantal point processes with sublinear time preprocessing. Advances in Neural Information Processing Systems 32 (NeurIPS)
2019
Closest in time.
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2014
Cited alongside, same era.
Cohen MB, Musco C, Pachocki J (2015) Online row sampling. In: Proceedings of the 19th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX)
2015
Cited alongside, same era.
El Alaoui A, Mahoney M (2015) Fast randomized kernel ridge regression with statistical guarantees. In: Advances in Neural Information Processing Systems 28, pp 775–783
2015
Cited alongside, same era.
Liang D, Paisley J (2015) Landmarking manifolds with gaussian processes. In: Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, vol 37, pp 466–474
2015
Cited alongside, same era.
Gittens A, Mahoney MW (2016) Revisiting the Nyström method for improved large-scale machine learning. Journal of Machine Learning Research 17:117:1–117:65
2016
Cited alongside, same era.
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
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Minsker S (2017) On some extensions of bernstein’s inequality for self-adjoint operators. Statistics & Probability Letters 127:111 – 119
2017
Cited alongside, same era.
Gao T, Kovalsky S, Daubechies I (2019) Gaussian process landmarking on manifolds. SIAM Journal on Mathematics of Data Science 1(1):208–236
2019
Closest in time.
Gartrell M, Brunel VE, Dohmatob E, Krichene S (2019) Learning nonsymmetric determinantal point processes. In: Advances in Neural Information Processing Systems, Curran Associates, Inc., vol 32, pp 6718–6728
2019
Closest in time.
Tremblay N, Barthelmé S, Amblard P (2019) Determinantal point processes for coresets. Journal of Machine Learning Research 20(168):1–70
2019
Closest in time.
Tropp J (2019) Matrix concentration & computational linear algebra, Teaching Resource (Unpublished)
2019
Closest in time.
Feldman V (2020) Does learning require memorization? a short tale about a long tail. In: Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, pp 954–959
2020
Closest in time.
Launay C, Galerne B, Desolneux A (2020) Exact sampling of determinantal point processes without eigendecomposition. Journal of Applied Probability 57(4):1198–1221
2020
Closest in time.
Oakden-Rayner L, Dunnmon J, Carneiro G, Ré C (2020) Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In: Proceedings of the ACM conference on health, inference, and learning, pp 151–159
2020
Closest in time.
Poulson J (2020) High-performance sampling of generic determinantal point processes. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378(2166):20190059
2020
Closest in time.
Schreurs J, Fanuel M, Suykens J (2020) Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes. ICML 2020 workshop on Negative Dependence and Submodularity, PMLR 119
2020
Closest in time.
Fanuel M, Schreurs J, Suykens J (2021) Diversity sampling is an implicit regularization for kernel methods. SIAM Journal on Mathematics of Data Science 3(1):280–297
2021
Closest in time.
Li C, Jegelka S, Sra S (2016b) Fast DPP sampling for Nyström with application to kernel methods. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48, ICML’16, pp 2061–2070
2070
Closest in time.
Gong B, Chao WL, Grauman K, Sha F (2014) Diverse sequential subset selection for supervised video summarization. In: Advances in Neural Information Processing Systems, pp 2069–2077
2077
Closest in time.
Zhang T (2005) Learning bounds for kernel regression using effective data dimensionality. Neural Computation 17(9):2077–2098
2098
Closest in time.