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Michał Dereziński
Email: derezin at umich edu I am an Assistant Professor of Computer Science and Engineering at the University of Michigan. Previously, I was a postdoctoral fellow in the Department of Statistics at the University of California, Berkeley, and a research fellow at the Simons Institute for the Theory of Computing. I obtained my Ph.D. in Computer Science at the University of California, Santa Cruz, advised by professor Manfred Warmuth. My research is focused on the theoretical foundations of randomized algorithms for numerical linear algebra, machine learning, optimization, and data science. Prior to UCSC, I completed Master's degrees in mathematics and computer science at the University of Warsaw. Research interests: Theoretical computer science, randomized numerical linear algebra (RandNLA), stochastic optimization, machine learning (ML), and random matrix theory (RMT). To learn more about some of my interests, check out this tutorial on RandNLA for ML. |
Students |
Current students:
Joining my group: If you are not a UM student, apply directly to CSE graduate admissions and list my name in the application. If you are a UM student, feel free to reach out to me over email, but there is a good chance I will not be able to respond. Fraudulent job offers: If you receive an email with my name on it that mentions a job offer you did not apply for, it is most likely a scam. Here is more information about this type of scam. |
Updates |
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Teaching |
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Publications |
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Randomized Kaczmarz Methods with Beyond-Krylov Convergence
Optimal Oblivious Subspace Embeddings with Near-optimal Sparsity
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
Stochastic Newton Proximal Extragradient Method
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
Distributed Least Squares in Small Space via Sketching and Bias Reduction
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
Solving Dense Linear Systems Faster than via Preconditioning
Optimal Embedding Dimension for Sparse Subspace Embeddings
Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs
Randomized Numerical Linear Algebra - A Perspective on the Field with an Eye to Software
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
Unbiased estimators for random design regression
Domain Sparsification of Discrete Distributions using Entropic Independence
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
Query Complexity of Least Absolute Deviation Regression
via Robust Uniform Convergence
Sparse sketches with small inversion bias
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
Determinantal Point Processes
in Randomized Numerical Linear Algebra
Debiasing Distributed Second Order Optimization with Surrogate Sketching
and Scaled Regularization
Sampling from a k-DPP without looking at all items
Precise expressions for random projections: Low-rank approximation and
randomized Newton
Improved guarantees and a multiple-descent curve for
Column Subset Selection and the Nyström method
Exact expressions for double descent and implicit regularization via surrogate random design
Isotropy and Log-Concave Polynomials: Accelerated Sampling and High-Precision Counting of Matroid Bases
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
Bayesian experimental design using regularized determinantal point processes
Exact sampling of determinantal point processes with sublinear time preprocessing
Distributed estimation of the inverse Hessian by determinantal averaging
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Fast determinantal point processes via distortion-free intermediate sampling
Correcting the bias in least squares regression with volume-rescaled sampling
Leveraged volume sampling for linear regression
Reverse iterative volume sampling for linear regression
Subsampling for Ridge Regression via Regularized Volume Sampling
Batch-Expansion Training: An Efficient Optimization Framework
Discovering Surprising Documents with Context-Aware Word Representations
Unbiased estimates for linear regression via volume sampling
The limits of squared Euclidean distance regularization |