← Authors

Michal Derezinski

19 papers · 2014–2025 · 4 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (4) πŸ—ΊοΈ Taxonomy Completionist (35) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (4) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (11) 🧭 Keyword Pioneer πŸ† Keyword Champion πŸ”₯ Unstoppable (5) πŸ’Ž Century Club (19) ⚑ Prolific Year (7) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (84)

Conferences

NIPS (11) AISTATS (5) COLT (2) JMLR (1)

Top co-authors

Michael W. Mahoney (6) Manfred K. Warmuth (4) Mert Pilanci (2) Daniele Calandriello (2) Feynman T Liang (2) Zhenyu Liao (2) Michael Mahoney (2) Michal Valko (2) Jonathan Lacotte (1) Rajiv Khanna (1)

Keywords

determinantal point process (6) linear regression (6) volume sampling (3) convergence rate (3) unbiased estimator (3) kernel methods (2) sampling algorithm (2) distributed optimization (2) matrix sketching (2) least square (2) low-rank approximation (2) second-order optimization (2) spectral analysis (2) convex optimization (2) subset selection (2) leverage score sampling (2) coordinate descent (1) uniform convergence (1) subsampling methods (1) importance sampling (1)

Papers

Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems JMLR 2025
Sparse sketches with small inversion bias COLT 2021
Query complexity of least absolute deviation regression via robust uniform convergence COLT 2021
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update NIPS 2021
Sampling from a k-DPP without looking at all items NIPS 2020
Precise expressions for random projections: Low-rank approximation and randomized Newton NIPS 2020
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method NIPS 2020
Exact expressions for double descent and implicit regularization via surrogate random design NIPS 2020
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization NIPS 2020
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling AISTATS 2020
Bayesian experimental design using regularized determinantal point processes AISTATS 2020
Correcting the bias in least squares regression with volume-rescaled sampling AISTATS 2019
Exact sampling of determinantal point processes with sublinear time preprocessing NIPS 2019
Distributed estimation of the inverse Hessian by determinantal averaging NIPS 2019
Subsampling for Ridge Regression via Regularized Volume Sampling AISTATS 2018
Batch-Expansion Training: An Efficient Optimization Framework AISTATS 2018
Leveraged volume sampling for linear regression NIPS 2018
Unbiased estimates for linear regression via volume sampling NIPS 2017
The limits of squared Euclidean distance regularization NIPS 2014