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While leverage score sampling provides powerful tools for approximating solutions to large least squares problems, the cost of computing exact scores and sampling often prohibits practical application.
1909
Earlier work this paper cites.
Ishigami, T., Homma, T.: An importance quantification technique in uncertainty analysis for computer models. In: [1990] Proceedings. First International Symposium on Uncertainty Modeling and Analysis, pp. 398–403. IEEE Comput. Soc. Press, (1990)
1990
Earlier work this paper cites.
Fausett, D.W., Fulton, C.T.: Large Least Squares Problems Involving Kronecker Products. SIAM Journal on Matrix Analysis and Applications 15
1994
Earlier work this paper cites.
Fausett, D.W., Fulton, C.T., Hashish, H.: Improved parallel QR method for large least squares problems involving Kronecker products. Journal of Computational and Applied Mathematics 78
1997
Earlier work this paper cites.
2002
Earlier work this paper cites.
Ghanem, R.G., Spanos, P.D.: Stochastic Finite Elements: a Spectral Approach. Courier Corporation, (2003)
2003
Earlier work this paper cites.
Drineas, P., Mahoney, M.W., Muthukrishnan, S.: Sampling algorithms for ℓ 2 \ell_{2} regression and applications. In: Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithm, pp. 1127–1136 (2006)
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
Drineas, P., Mahoney, M.W., Muthukrishnan, S.: Relative-error CUR matrix decompositions. SIAM Journal on Matrix Analysis and Applications 30
2008
Earlier work this paper cites.
Ailon, N., Chazelle, B.: The fast Johnson–Lindenstrauss transform and approximate nearest neighbors. SIAM Journal on Computing 39
2009
Earlier work this paper cites.
Drineas, P., Mahoney, M.W., Muthukrishnan, S., Sarlós, T.: Faster least squares approximation. Numerische Mathematik 117
2011
Earlier work this paper cites.
Drineas, P., Magdon-Ismail, M., Mahoney, M.W., Woodruff, D.P.: Fast approximation of matrix coherence and statistical leverage. The Journal of Machine Learning Research 13
2012
Earlier work this paper cites.
Pagh, R.: Compressed Matrix Multiplication. ACM Transactions on Computation Theory 5
2013
Earlier work this paper cites.
Pham, N., Pagh, R.: Fast and Scalable Polynomial Kernels via Explicit Feature Maps. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’13, pp. 239–247. ACM, New York, NY, USA (2013). https://doi.org/10.1145/2487575.2487591
2013
Earlier work this paper cites.
Nelson, J., Nguyên, H.L.: Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings. In: 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 117–126 (2013). https://doi.org/10.1109/FOCS.2013.21
2013
Earlier work this paper cites.
Sankararaman, S., Goebel, K.: Uncertainty quantification in remaining useful life of aerospace components using state space models and inverse form. (2013)
2013
Earlier work this paper cites.
Biagioni, D.J., Beylkin, D., Beylkin, G.: Randomized interpolative decomposition of separated representations. Journal of Computational Physics 281
2014
Earlier work this paper cites.
Avron, H., Nguyen, H.L., Woodruff, D.P.: Subspace Embeddings for the Polynomial Kernel. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, pp. 2258–2266. MIT Press, Cambridge, MA, USA (2014)
2014
Cited alongside, same era.
Sankararaman, S., Daigle, M., Goebel, K.: Uncertainty quantification in remaining useful life prediction using first-order reliability methods. Reliability, IEEE Transactions on 63
2014
Cited alongside, same era.
Sankararaman, S.: Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing 52-53
2014
Cited alongside, same era.
Mai, C.V., Sudret, B.: Polynomial chaos expansions for damped oscillators. In: 12th International Conference on Applications of Statistics and Probability in Civil Engineering, Vancouver, Canada (2015). https://hal.archives-ouvertes.fr/hal-01169245
2015
Cited alongside, same era.
Ahle, T.D., Kapralov, M., Knudsen, J.B., Pagh, R., Velingker, A., Woodruff, D.P., Zandieh, A.: Oblivious sketching of high-degree polynomial kernels. In: Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 141–160. SIAM, (2020)
2020
Later among the works it cites.
Woodruff, D., Zandieh, A.: Near input sparsity time kernel embeddings via adaptive sampling. In: Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 10324–10333. PMLR, (2020)
2020
Later among the works it cites.
Chen, K., Li, Q., Newton, K., Wright, S.J.: Structured random sketching for PDE inverse problems. SIAM Journal on Matrix Analysis and Applications 41
2020
Later among the works it cites.
Fausett, D.W., Hashish, H.: Overview of QR Methods for Large Least Squares Problems Involving Kronecker Products. In: Overview of QR Methods for Large Least Squares Problems Involving Kronecker Products, pp. 71–80. De Gruyter, (2020). https://doi.org/10.1515/9783112314098-009
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2015
Cited alongside, same era.
Cheng, D., Peng, R., Liu, Y., Perros, I.: SPALS: Fast alternating least squares via implicit leverage scores sampling. In: Advances In Neural Information Processing Systems, pp. 721–729 (2016)
2016
Cited alongside, same era.
Clarkson, K.L., Woodruff, D.P.: Low-Rank Approximation and Regression in Input Sparsity Time. Journal of the ACM 63
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Sun, Y., Guo, Y., Tropp, J.A., Udell, M.: Tensor random projection for low memory dimension reduction. In: NeurIPS Workshop on Relational Representation Learning (2018)
2018
Cited alongside, same era.
Battaglino, C., Ballard, G., Kolda, T.G.: A practical randomized CP tensor decomposition. SIAM Journal on Matrix Analysis and Applications 39
2018
Cited alongside, same era.
Diao, H., Song, Z., Sun, W., Woodruff, D.: Sketching for Kronecker Product Regression and P-splines. In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, pp. 1299–1308 (2018)
2018
Cited alongside, same era.
Diaz, P., Doostan, A., Hampton, J.: Sparse polynomial chaos expansions via compressed sensing and d-optimal design. Computer Methods in Applied Mechanics and Engineering 336
2018
Cited alongside, same era.
2020
Later among the works it cites.
Rakhshan, B.T., Rabusseau, G.: Rademacher random projections with tensor networks. In: NeurIPS Workshop on Quantum Tensor Networks in Machine Learning (2021)
2021
Later among the works it cites.
Iwen, M.A., Needell, D., Rebrova, E., Zare, A.: Lower memory oblivious (tensor) subspace embeddings with fewer random bits: Modewise methods for least squares. SIAM Journal on Matrix Analysis and Applications 42
2021
Later among the works it cites.
2021
Later among the works it cites.
Song, Z., Woodruff, D., Yu, Z., Zhang, L.: Fast sketching of polynomial kernels of polynomial degree. In: Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 9812–9823. PMLR, (2021)
2021
Later among the works it cites.
Malik, O.A., Becker, S.: A sampling-based method for tensor ring decomposition. In: Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 7400–7411. PMLR, (2021). https://proceedings.mlr.press/v139/malik21b.html
2021
Later among the works it cites.
Adcock, B., Brugiapaglia, S., Webster, C.G.: Sparse Polynomial Approximation of High-Dimensional Functions. SIAM, (2022)
2022
Closest in time.
2022
Closest in time.
2022
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Malik, O.A.: More efficient sampling for tensor decomposition with worst-case guarantees. In: Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 162, pp. 14887–14917. PMLR, (2022). https://proceedings.mlr.press/v162/malik22a.html
2022
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Woodruff, D., Zandieh, A.: Leverage score sampling for tensor product matrices in input sparsity time. In: Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 162, pp. 23933–23964. PMLR, (2022)
2022
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2023
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Meyer, R.A., Musco, C., Musco, C., Woodruff, D.P., Zhou, S.: Near-linear sample complexity for lp polynomial regression. In: Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 3959–4025 (2023). SIAM
2023
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