Michal Derezinski
19 papers · 2014–2025 · 4 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (35) π Academic Marathon Β· Half π Conference Polyglot Β· Bronze π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Deep Specialist
(10)
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Keyword Champion
π§
Keyword Pioneer
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Century Club Β· Bronze
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Unstoppable Β· Bronze
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Prolific Year Β· Productive
ποΈ
Keyword Collector
(92)
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Trend Setter
Venues
Top co-authors
Research topics
Keywords
linear regression
(6)
determinantal point process
(6)
volume sampling
(3)
convergence rate
(3)
unbiased estimator
(3)
subset selection
(2)
sampling algorithm
(2)
distributed optimization
(2)
matrix sketching
(2)
least square
(2)
low-rank approximation
(2)
second-order optimization
(2)
kernel methods
(2)
convex optimization
(2)
spectral analysis
(2)
leverage score sampling
(2)
uniform convergence
(1)
importance sampling
(1)
subsampling methods
(1)
covariance matrix
(1)
Papers
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
JMLR 2025
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
NeurIPS 2021
Query complexity of least absolute deviation regression via robust uniform convergence
COLT 2021
Sparse sketches with small inversion bias
COLT 2021
Bayesian experimental design using regularized determinantal point processes
AISTATS 2020
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
AISTATS 2020
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
NeurIPS 2020
Exact expressions for double descent and implicit regularization via surrogate random design
NeurIPS 2020
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
NeurIPS 2020
Precise expressions for random projections: Low-rank approximation and randomized Newton
NeurIPS 2020
Sampling from a k-DPP without looking at all items
NeurIPS 2020
Correcting the bias in least squares regression with volume-rescaled sampling
AISTATS 2019
Distributed estimation of the inverse Hessian by determinantal averaging
NeurIPS 2019
Exact sampling of determinantal point processes with sublinear time preprocessing
NeurIPS 2019
Batch-Expansion Training: An Efficient Optimization Framework
AISTATS 2018
Leveraged volume sampling for linear regression
NeurIPS 2018
Subsampling for Ridge Regression via Regularized Volume Sampling
AISTATS 2018
Unbiased estimates for linear regression via volume sampling
NeurIPS 2017
The limits of squared Euclidean distance regularization
NeurIPS 2014