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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 (Fall 2018, Foundations of Data Science program). 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 developing scalable randomized algorithms with robust statistical guarantees for machine learning, data science and optimization. Prior to UCSC, I completed Master's degrees in mathematics and computer science at the University of Warsaw. I also interned at a variety of Silicon Valley research labs (e.g. Microsoft, Yahoo, eBay), working on projects ranging from online learning to large-scale distributed optimization. Research interests: Machine learning theory and algorithms, high-dimensional statistics, continuous and combinatorial optimization, randomized linear algebra, determinantal point processes, random matrix theory |
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Publications |
2021
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
Unbiased estimators for random design regression
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
Anticipating Concept Drift in Online Learning
The limits of squared Euclidean distance regularization
Active Semi-Supervised Concept Hierarchy Refinement |