Michal Derezinski
19 papers · 2014–2025 · 4 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (11) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (4) 🐝 Cross-Pollinator (4)
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Academic Marathon
(11)
🧭
Keyword Pioneer
🏆
Keyword Champion
🔥
Unstoppable
(5)
💎
Century Club
(19)
⚡
Prolific Year
(7)
📈
Trend Setter
🗃️
Keyword Collector
(84)
Conferences
NIPS (11)
AISTATS (5)
COLT (2)
JMLR (1)
Top co-authors
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