Michał Dereziński

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Michael MahoneyProfessor of Statistics, UC BerkeleyVerified email at stat.berkeley.edu
Manfred K. Warmuthvisiting Google BrainVerified email at google.com
Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
Feynman LiangStatistics PhD Student at University of California, BerkeleyVerified email at berkeley.edu
Rajiv KhannaAssistant Prof, PurdueCSVerified email at purdue.edu
Michal ValkoChief Models Officer @ Stealth Startup, Inria & MVA - Ex: Llama at Meta; Gemini and BYOL @ DeepmindVerified email at meta.com
Daniele CalandrielloResearch Scientist, DeepMindVerified email at google.com
Mert PilanciStanford UniversityVerified email at stanford.edu
Zhenyu LiaoHuazhong University of Science and TechnologyVerified email at hust.edu.cn
Osman Asif MalikEncube TechnologiesVerified email at getencube.com
Riley MurraySandia National LaboratoriesVerified email at sandia.gov
Maksim MelnichenkoUniversity of Tennessee Knoxville, Innovative Computing LaboratoryVerified email at vols.utk.edu
James W DEMMELProfessor of EECS and Mathematics, UC BerkeleyVerified email at berkeley.edu
Elizaveta RebrovaPrinceton ORFEVerified email at princeton.edu
Mojmír MutnýETH ZurichVerified email at inf.ethz.ch
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Nima AnariStanford UniversityVerified email at cs.stanford.edu
Vipul GuptaGraduate student, EECS, UC BerkeleyVerified email at berkeley.edu
Piotr LuszczekUniversity of TennesseeVerified email at eecs.utk.edu
N. Benjamin ErichsonResearch ScientistVerified email at lbl.gov

Michał Dereziński

Assistant Professor at University of Michigan

Verified email at umich.edu - Homepage

machine learning optimization statistics randomized algorithms


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Determinantal point processes in randomized numerical linear algebra M Derezinski, MW Mahoney Notices of the American Mathematical Society 68 (1), 34-45, 2021	91	2021
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	85	2020
Leveraged volume sampling for linear regression M Derezinski, MKK Warmuth, DJ Hsu Advances in Neural Information Processing Systems 31, 2018	73	2018
Exact sampling of determinantal point processes with sublinear time preprocessing M Derezinski, D Calandriello, M Valko Advances in neural information processing systems 32, 2019	63	2019
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	57*	2023
Unbiased estimates for linear regression via volume sampling M Derezinski, MKK Warmuth Advances in Neural Information Processing Systems 30, 2017	55	2017
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	53	2020
Reverse iterative volume sampling for linear regression M Dereziński, MK Warmuth Journal of Machine Learning Research 19 (23), 1-39, 2018	50	2018
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	35	2021
Query complexity of least absolute deviation regression via robust uniform convergence X Chen, M Derezinski Conference on Learning Theory, 1144-1179, 2021	34	2021
Fast determinantal point processes via distortion-free intermediate sampling M Dereziński Conference on Learning Theory, 1029-1049, 2019	34	2019
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	33	2020
Distributed estimation of the inverse hessian by determinantal averaging M Derezinski, MW Mahoney Advances in Neural Information Processing Systems 32, 2019	32	2019
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	31	2020
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	30	2020
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	30	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	28	2019
Subsampling for ridge regression via regularized volume sampling M Dereziński, MK Warmuth International Conference on Artificial Intelligence and Statistics, 2017	27	2017
Sparse sketches with small inversion bias M Derezinski, Z Liao, E Dobriban, M Mahoney Conference on Learning Theory, 1467-1510, 2021	26	2021
Bayesian experimental design using regularized determinantal point processes M Derezinski, F Liang, M Mahoney International Conference on Artificial Intelligence and Statistics, 3197-3207, 2020	25	2020

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