Skip to content

Home Search Authors Pakistan News Saved

SciRah

SciRah does not host or store research papers. Results come live from free, official scholarly APIs, and every paper links back to its original source.

Data sources

OpenAlex
Semantic Scholar
Crossref
CORE
arXiv
PubMed

Privacy policy Terms of service

Michal Derezinski · SciRah

Researcher profile · Semantic Scholar

Michal Derezinski

Share profile

Michal Derezinski — researcher profile on SciRah

h-index: 22
Works: 41
Citations: 1,170

Most-cited papers

Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software
Riley Murray, J. Demmel, Michael W. Mahoney, N. Benjamin Erichson +9 more
2023arXiv.org130 citations Open access
Semantic Scholar
Determinantal Point Processes in Randomized Numerical Linear Algebra
Michal Derezinski, Michael W. Mahoney
2020Notices of the American Mathematical Society103 citations Open access
Semantic Scholar
Exact expressions for double descent and implicit regularization via surrogate random design
Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
2019Neural Information Processing Systems81 citations
Semantic Scholar
Leveraged volume sampling for linear regression
Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
2018Neural Information Processing Systems68 citations
Semantic Scholar
Exact sampling of determinantal point processes with sublinear time preprocessing
Michal Derezinski, Daniele Calandriello, Michal Valko
2019Neural Information Processing Systems62 citations
Semantic Scholar
Unbiased estimates for linear regression via volume sampling
Michal Derezinski, Manfred K. Warmuth
2017Neural Information Processing Systems60 citations
Semantic Scholar
Reverse iterative volume sampling for linear regression
Michal Derezinski, Manfred K. Warmuth
2018Journal of machine learning research52 citations
Semantic Scholar
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
Michal Derezinski, Rekha Khanna, Michael W. Mahoney
2020Neural Information Processing Systems42 citations Open access
Semantic Scholar
Fast determinantal point processes via distortion-free intermediate sampling
Michal Derezinski
2018Annual Conference Computational Learning Theory41 citations
Semantic Scholar
Query Complexity of Least Absolute Deviation Regression via Robust Uniform Convergence
Xue Chen, Michal Derezinski
2021Annual Conference Computational Learning Theory35 citations
Semantic Scholar
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
Michal Derezinski, Jonathan Lacotte, Mert Pilanci, Michael W. Mahoney
2021Neural Information Processing Systems33 citations
Semantic Scholar
Distributed estimation of the inverse Hessian by determinantal averaging
Michal Derezinski, Michael W. Mahoney
2019Neural Information Processing Systems32 citations
Semantic Scholar
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition
Michal Derezinski, E. Rebrova
2022SIAM Journal on Mathematics of Data Science31 citations Open access
Semantic Scholar
Sampling from a k-DPP without looking at all items
Daniele Calandriello, Michal Derezinski, Michal Valko
2020Neural Information Processing Systems30 citations
Semantic Scholar
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Michal Derezinski, K. Clarkson, Michael W. Mahoney, Manfred K. Warmuth
2019Annual Conference Computational Learning Theory29 citations
Semantic Scholar
Bayesian experimental design using regularized determinantal point processes
Michal Derezinski, Feynman T. Liang, Michael W. Mahoney
2019International Conference on Artificial Intelligence and Statistics29 citations
Semantic Scholar
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney
2020Neural Information Processing Systems28 citations
Semantic Scholar
Hessian averaging in stochastic Newton methods achieves superlinear convergence
Sen Na, Michal Derezinski, Michael W. Mahoney
2022Mathematical programming27 citations Open access
Semantic Scholar
Sparse sketches with small inversion bias
Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney
2020Annual Conference Computational Learning Theory27 citations
Semantic Scholar
Precise expressions for random projections: Low-rank approximation and randomized Newton
Michal Derezinski, Feynman T. Liang, Zhenyu A. Liao, Michael W. Mahoney
2020Neural Information Processing Systems26 citations
Semantic Scholar
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
Mojm'ir Mutn'y, Michal Derezinski, A. Krause
2019International Conference on Artificial Intelligence and Statistics25 citations
Semantic Scholar
Unbiased estimators for random design regression
Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
2019Journal of machine learning research22 citations
Semantic Scholar
Isotropy and Log-Concave Polynomials: Accelerated Sampling and High-Precision Counting of Matroid Bases
Nima Anari, Michal Derezinski
2020IEEE Annual Symposium on Foundations of Computer Science21 citations Open access
Semantic Scholar
Subsampling for Ridge Regression via Regularized Volume Sampling
Michal Derezinski, Manfred K. Warmuth
2017International Conference on Artificial Intelligence and Statistics20 citations
Semantic Scholar
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna +2 more
2021arXiv.org16 citations
Semantic Scholar
Correcting the bias in least squares regression with volume-rescaled sampling
Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
2018International Conference on Artificial Intelligence and Statistics15 citations
Semantic Scholar
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
Michal Derezinski
2022Trans. Mach. Learn. Res.11 citations Open access
Semantic Scholar
Domain Sparsification of Discrete Distributions using Entropic Independence
Nima Anari, Michal Derezinski, T. Vuong, E. Yang
2021Information Technology Convergence and Services11 citations
Semantic Scholar
LocalNewton: Reducing communication rounds for distributed learning
Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna +2 more
2021Conference on Uncertainty in Artificial Intelligence10 citations
Semantic Scholar
Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method
Michal Derezinski, Rajiv Khanna, Michael W. Mahoney
2020arXiv.org10 citations
Semantic Scholar
The limits of squared Euclidean distance regularization
Michal Derezinski, Manfred K. Warmuth
2014Neural Information Processing Systems9 citations
Semantic Scholar
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs
Michal Derezinski
2022Annual Conference Computational Learning Theory7 citations Open access
Semantic Scholar
Batch-Expansion Training: An Efficient Optimization Framework
Michal Derezinski, D. Mahajan, S. Keerthi, S. Vishwanathan +1 more
2017International Conference on Artificial Intelligence and Statistics7 citations
Semantic Scholar
Convergence Analysis of the Randomized Newton Method with Determinantal Sampling
Mojm'ir Mutn'y, Michal Derezinski, A. Krause
2019arXiv.org6 citations
Semantic Scholar
Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems
Younghyun Cho, J. Demmel, Michal Derezinski, Hao-Yun Li +3 more
2023arXiv.org5 citations Open access
Semantic Scholar
Tail bounds for volume sampled linear regression
Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
2018arXiv.org5 citations
Semantic Scholar
Discovering Surprising Documents with Context-Aware Word Representations
Michal Derezinski, Khashayar Rohanimanesh, Aamer Hydrie
2018International Conference on Intelligent User Interfaces3 citations
Semantic Scholar
Volume sampling for linear regression
Michal Derezinski
20182 citations
Semantic Scholar
Batch-Expansion Training: An Efficient Optimization Paradigm for Machine Learning
Michal Derezinski, D. Mahajan, S. Keerthi, S. Vishwanathan +1 more
2017arXiv.org1 citations
Semantic Scholar
Inference for Newton Methods with Accelerated Sketch-and-Project via Random Scaling
Xin-Cheng Du, E. Rebrova, Michal Derezinski, Sen Na
20260 citations
Semantic Scholar
Debiasing Random Oblique Projections for Subsampled OLS and Fast CUR in High Dimensions
Chengmei Niu, Sachin Garg, Michal Derezinski, Zhenyu Liao
2026arXiv.org0 citations
Semantic Scholar

Frequent co-authors

Invite them to SciRah by sharing their profile.

Michael W. Mahoney×12
Invite
Michael W. Mahoney — researcher profile on SciRah
Manfred K. Warmuth×9
Invite
Manfred K. Warmuth — researcher profile on SciRah
Michael W. Mahoney×4
Invite
Michael W. Mahoney — researcher profile on SciRah
Daniel J. Hsu×4
Invite
Daniel J. Hsu — researcher profile on SciRah

Feynman T. Liang×3

Invite

Feynman T. Liang — researcher profile on SciRah

Riley Murray×2

Invite

Riley Murray — researcher profile on SciRah

Invite

Hengrui Luo — researcher profile on SciRah

Daniele Calandriello×2

Invite

Daniele Calandriello — researcher profile on SciRah

Michal Valko×2

Invite

Michal Valko — researcher profile on SciRah

Mert Pilanci×2

Invite

Mert Pilanci — researcher profile on SciRah

Invite

E. Rebrova — researcher profile on SciRah

Mojm'ir Mutn'y×2

Invite

Mojm'ir Mutn'y — researcher profile on SciRah