Michał Dereziński

Email: mderezin@ucsc.edu
Website: http://users.soe.ucsc.edu/~mderezin

I am currently in the fifth year of my Ph.D. at the University of California, Santa Cruz, advised by professor Manfred Warmuth. In my thesis research, I develop efficient data sampling techniques with applications to learning theory 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.


Education and Research

CV

Ph.D. student, Computer Science, University of California, Santa Cruz.

M.S. in Computer Science, University of Warsaw.
Thesis: On Generating Concept Hierarchies with Fuzzy Data  PDF

M.S. in Mathematics, University of Warsaw.
Thesis: Isomorphic Properties of Function Space BV on Simply Connected Planar Sets  PDF




Tail bounds for volume sampled linear regression
M. Dereziński, M. Warmuth, D. Hsu
2018  arXiv:1802.06749
Proposed a new variant of volume sampling, which achieves strong tail bounds for linear regression, while maintaining all of the benefits of joint sampling. Also, developed new techniques for proving tail bounds for the sums of dependent random matrices that arise from volume sampling.


Subsampling for Ridge Regression via Regularized Volume Sampling
M. Dereziński, M. Warmuth
AISTATS 2018 (to appear)  arXiv:1710.05110  Poster
Proposed a new efficient procedure for solving a ridge regression task over a large unlabeled dataset, when computing labels is expensive. This method, called regularized volume sampling, is fast, easy to implement, and offers strong statistical guarantees.


Batch-Expansion Training: An Efficient Optimization Framework
M. Dereziński, D. Mahajan, S. S. Keerthi, S.V.N. Vishwanathan, M. Weimer
AISTATS 2018 (to appear)  arXiv:1704.06731
Proposed a parameter-free batch optimization method running on a gradually expanding dataset, which outperforms standard batch and stochastic approaches in large-scale settings.


Discovering Surprising Documents with Context-Aware Word Representations
M. Dereziński, K. Rohanimanesh, A. Hydrie
IUI 2018 (to appear)  PDF
Developed a information-theoretic model for discovering surprise in text, that outperforms deep learning in a practical task.


Unbiased estimates for linear regression via volume sampling
M. Dereziński, M. Warmuth
NIPS 2017 (in submission for JMLR)  arXiv:1705.06908  Poster  Spotlight
Showed that volume sampling provides an unbiased estimate of the least squares solution from a subset of labels, which is close to the optimum solution up to a multiplicative factor. Also, proposed an efficient algorithm for volume sampling.


Anticipating Concept Drift in Online Learning
M. Dereziński, B. N. Bhaskar
LFED Workshop at NIPS 2015  PDF
Developed and analyzed online optimization algorithms that dynamically adapt to concept drift.


The limits of squared Euclidean distance regularization
M. Dereziński, M. Warmuth
NIPS 2014  PDF  Spotlight
Proved a lower bound for the loss of learning algorithms that regularize with squared Euclidean distance, for a certain family of learning problems. Proposed and experimentally tested a conjecture extending the result to deep learning.


Active Semi-Supervised Concept Hierarchy Refinement
M. Dereziński
LAWS 2012 workshop  PDF
Developed and tested a new algorithm for refining concept hierarchies generated from unsupervised data, by actively querying a human expert.



Experience

Research Intern, Microsoft Research, Sunnyvale, CA
Developed a new optimization framework combining the benefits of stochastic and batch methods.

Intern Research Scientist, Yahoo Inc., Sunnyvale, CA
Developed optimization algorithms for recommendation systems that dynamically adapt to changing environment.

Intern Research Scientist, eBay Inc., San Jose, CA
Developed and implemented an unsupervised recommendation system based on topic modeling, for finding interesting products.

Software engineer Intern, Nvidia, Santa Clara, CA.

Intern, Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw, Poland.
Researched algorithms related to concurrent programming.