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# [2019-12-06] Prof. Min-Hsiu Hsieh, University of Technology Sydney, "On Dimension-free Tail Inequalities for Sums of Random Matrices and Applications"

專題討論演講公告

Poster： ╱ Post date：2019-11-26**Title:**On Dimension-free Tail Inequalities for Sums of Random Matrices and Applications

**Date:**2019-12-06 10:20am-11:30am

**Location:**R210, CSIE

**Speaker:**Prof. Min-Hsiu Hsieh, University of Technology Sydney

**Hosted by:**Prof. Yen-Huan Li

**Abstract:**

In this paper, we present a new framework to obtain tail inequalities for sums of random matrices. Compared with existing works, our tail inequalities have the following characteristics: 1) high feasibility---they can be used to study the tail behavior of various matrix functions, e.g., arbitrary matrix norms, the absolute value of the sum of the sum of the j largest singular values (resp. eigenvalues) of complex matrices (resp. Hermitian matrices); and 2) independence of matrix dimension---they do not have the matrix-dimension term as a product factor, and thus are suitable to the scenario of high-dimensional or infinite-dimensional random matrices. The price we pay to obtain these advantages is that the convergence rate of the resulting inequalities will become slow when the number of summand random matrices is large. We also develop the tail inequalities for matrix random series and matrix martingale difference sequence. We also demonstrate the usefulness of our tail bounds in several fields. In compressed sensing, we employ the resulted tail inequalities to achieve a proof of the restricted isometry property when the measurement matrix is the sum of random matrices without any assumption on the distributions of matrix entries. In probability theory, we derive a new upper bound to the supreme of stochastic processes. In machine learning, we prove new expectation bounds of sums of random matrices matrix and obtain matrix approximation schemes via random sampling. In quantum information, we show a new analysis relating to the fractional cover number of quantum hypergraphs. In theoretical computer science, we obtain randomness-efficient samplers using matrix expander graphs that can be efficiently implemented in time without dependence on matrix dimensions.

**Biography:**

Min-Hsiu Hsieh received his BS and MS in electrical engineering from National Taiwan University in 1999 and 2001, and PhD degree in electrical engineering from the University of Southern California, Los Angeles, in 2008. From 2008-2010, he was a Researcher at the ERATO-SORST Quantum Computation and Information Project, Japan Science and Technology Agency, Tokyo, Japan. From 2010-2012, he was a Postdoctoral Researcher at the Statistical Laboratory, the Centre for Mathematical Sciences, the University of Cambridge, UK. He joined the University of Technology Sydney (UTS) as a lecturer in 2012, became a senior lecturer in 2013, and an associate professor in 2014. He received an Australian Research Council (ARC) Future Fellowship from 2014-2018. He is a core member and program leader at the UTS Centre for Quantum Software and Information. His scientific interests include quantum information, quantum learning, and quantum computation. He is an associate editor of IEEE Transactions on Information Theory for the area of quantum information theory. More information can be found at https://www.minhsiu.com/.

Last modification time：2019-11-26 PM 2:42