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Michał Dereziński
Michał Dereziński
Assistant Professor at University of Michigan
Correu electrònic verificat a umich.edu - Pàgina d'inici
Títol
Citada per
Citada per
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Determinantal point processes in randomized numerical linear algebra
M Derezinski, MW Mahoney
Notices of the American Mathematical Society 68 (1), 34-45, 2021
942021
Exact expressions for double descent and implicit regularization via surrogate random design
M Dereziński, F Liang, MW Mahoney
Advances in Neural Information Processing Systems 33, 2020
872020
Leveraged volume sampling for linear regression
M Derezinski, MKK Warmuth, DJ Hsu
Advances in Neural Information Processing Systems 31, 2018
702018
Exact sampling of determinantal point processes with sublinear time preprocessing
M Derezinski, D Calandriello, M Valko
Advances in neural information processing systems 32, 2019
642019
Randomized numerical linear algebra: A perspective on the field with an eye to software
R Murray, J Demmel, MW Mahoney, NB Erichson, M Melnichenko, ...
arXiv preprint arXiv:2302.11474, 2023
632023
Unbiased estimates for linear regression via volume sampling
M Derezinski, MKK Warmuth
Advances in Neural Information Processing Systems 30, 2017
542017
Improved guarantees and a multiple-descent curve for column subset selection and the nystrom method
M Derezinski, R Khanna, MW Mahoney
Advances in Neural Information Processing Systems 33, 4953-4964, 2020
532020
Reverse iterative volume sampling for linear regression
M Dereziński, MK Warmuth
Journal of Machine Learning Research 19 (23), 1-39, 2018
502018
Newton-LESS: Sparsification without trade-offs for the sketched Newton update
M Derezinski, J Lacotte, M Pilanci, MW Mahoney
Advances in Neural Information Processing Systems 34, 2835-2847, 2021
362021
Distributed estimation of the inverse hessian by determinantal averaging
M Derezinski, MW Mahoney
Advances in Neural Information Processing Systems 32, 2019
352019
Precise expressions for random projections: Low-rank approximation and randomized Newton
M Derezinski, FT Liang, Z Liao, MW Mahoney
Advances in Neural Information Processing Systems 33, 18272-18283, 2020
342020
Fast determinantal point processes via distortion-free intermediate sampling
M Dereziński
Conference on Learning Theory, 1029-1049, 2019
342019
Query complexity of least absolute deviation regression via robust uniform convergence
X Chen, M Derezinski
Conference on Learning Theory, 1144-1179, 2021
332021
Debiasing distributed second order optimization with surrogate sketching and scaled regularization
M Derezinski, B Bartan, M Pilanci, MW Mahoney
Advances in Neural Information Processing Systems 33, 6684-6695, 2020
322020
Sampling from a k-DPP without looking at all items
D Calandriello, M Derezinski, M Valko
Advances in Neural Information Processing Systems 33, 6889-6899, 2020
312020
Convergence analysis of block coordinate algorithms with determinantal sampling
M Mutny, M Derezinski, A Krause
International Conference on Artificial Intelligence and Statistics, 3110-3120, 2020
29*2020
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least-squares regression
M Derezinski, KL Clarkson, MK Warmuth, M Mahoney
Conference on Learning Theory 99, 1-20, 2019
292019
Sparse sketches with small inversion bias
M Derezinski, Z Liao, E Dobriban, M Mahoney
Conference on Learning Theory, 1467-1510, 2021
272021
Subsampling for ridge regression via regularized volume sampling
M Dereziński, MK Warmuth
International Conference on Artificial Intelligence and Statistics, 2017
272017
LocalNewton: Reducing communication rounds for distributed learning
V Gupta, A Ghosh, M Dereziński, R Khanna, K Ramchandran, ...
Uncertainty in artificial intelligence, 632-642, 2021
25*2021
En aquests moments el sistema no pot dur a terme l'operació. Torneu-ho a provar més tard.
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