
Unbiased estimators for random design regression
In linear regression we wish to estimate the optimum linear least square...
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Distributed estimation of the inverse Hessian by determinantal averaging
In distributed optimization and distributed numerical linear algebra, we...
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Exact sampling of determinantal point processes with sublinear time preprocessing
We study the complexity of sampling from a distribution over all index s...
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Bayesian experimental design using regularized determinantal point processes
In experimental design, we are given n vectors in d dimensions, and our ...
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Convergence Analysis of the Randomized Newton Method with Determinantal Sampling
We analyze the convergence rate of the Randomized Newton Method (RNM) in...
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Exact expressions for double descent and implicit regularization via surrogate random design
Double descent refers to the phase transition that is exhibited by the g...
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Improved guarantees and a multipledescent curve for the Column Subset Selection Problem and the Nyström method
The Column Subset Selection Problem (CSSP) and the Nyström method are am...
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Precise expressions for random projections: Lowrank approximation and randomized Newton
It is often desirable to reduce the dimensionality of a large dataset by...
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Determinantal Point Processes in Randomized Numerical Linear Algebra
Randomized Numerical Linear Algebra (RandNLA) uses randomness to develop...
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Sampling from a kDPP without looking at all items
Determinantal point processes (DPPs) are a useful probabilistic model fo...
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Isotropy and LogConcave Polynomials: Accelerated Sampling and HighPrecision Counting of Matroid Bases
We define a notion of isotropy for discrete set distributions. If μ is a...
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Michał Dereziński
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